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Most video models do one thing well. MiniMax H3 does everything, together. Our next-gen open-weight multimodal model reads text, image, video, and audio as a single creative language: motion, sound, emotion, cinematography, all connected. The result: precision-level generation with real cinematic quality. → Commercial-grade: film, ads, MVs, UI, game...

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KOLOVESKI

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71,118 views • 1 day ago •via X (Twitter)

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MiniMax H3 is on Leonardo. Most video models give you a clip. H3 gives you the clip and the soundtrack — with your character and voice locked in from refs. Expect: - Commercial-grade quality across ads, e-commerce, gaming & UI - Best value in its class - One lightweight model, fully multimodal Built for one-shot finished content: brand teasers, product videos, fashion films, talking characters
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MiniMax H3 is on Leonardo. Most video models give you a clip. H3 gives you the clip and the soundtrack — with your character and voice locked in from refs. Expect: - Commercial-grade quality across ads, e-commerce, gaming & UI - Best value in its class - One lightweight model, fully multimodal Built for one-shot finished content: brand teasers, product videos, fashion films, talking characters

Leonardo.Ai

11,198 views • 2 days ago

Seedance 2.0 is now live on invideo for Max, Generative, and Team plan users. BytePlus's most advanced video model — and arguably the most controllable AI video model ever released. Multimodal input. Motion replication from reference videos. Native audio-video generation in one pass. Character consistency that actually holds. Director-level camera control. Real-world physics. This isn't prompt-and-pray. This is a production tool. Only available through business email verification for all regions except US and Japan.
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Seedance 2.0 is now live on invideo for Max, Generative, and Team plan users. BytePlus's most advanced video model — and arguably the most controllable AI video model ever released. Multimodal input. Motion replication from reference videos. Native audio-video generation in one pass. Character consistency that actually holds. Director-level camera control. Real-world physics. This isn't prompt-and-pray. This is a production tool. Only available through business email verification for all regions except US and Japan.

Invideo

5,552,011 views • 4 months ago

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.
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🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 views • 11 months ago

Dreamina Seedance 2.0 is Officially here! Dreamina Seedance 2.0, ByteDance’s AI-powered creative platform, lets creators easily transform ideas into high-quality videos. You can now edit videos like images, using up to 4 reference modalities (video, image, audio, and text) with precise control over visual effects, camera movements, and more. Why is this revolutionary? - 🔥 One-Prompt Video Editing: Edit videos seamlessly as easily as editing images. - 🎬 Multimodal Creativity: Combine images, video, audio, and text—up to 12 files at once. - 🎥 Remake Viral Content: Create high quality, professional-level videos with intelligent AI. - 🔧 Full Creative Control: Keep consistency in shots, typography, camera flow, and more. This isn’t just another video editing tool, it’s a one-stop AI workspace for all your creative needs. Ready to level up your content? Explore Dreamina Seedance 2.0 and start creating today. 🔗 #dreamina #seedance2 #seedream5 #dreaminatutorial #ai #aitools #aidesign #ecommercedesign #digitalmarketing #startupbusiness
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Dreamina Seedance 2.0 is Officially here! Dreamina Seedance 2.0, ByteDance’s AI-powered creative platform, lets creators easily transform ideas into high-quality videos. You can now edit videos like images, using up to 4 reference modalities (video, image, audio, and text) with precise control over visual effects, camera movements, and more. Why is this revolutionary? - 🔥 One-Prompt Video Editing: Edit videos seamlessly as easily as editing images. - 🎬 Multimodal Creativity: Combine images, video, audio, and text—up to 12 files at once. - 🎥 Remake Viral Content: Create high quality, professional-level videos with intelligent AI. - 🔧 Full Creative Control: Keep consistency in shots, typography, camera flow, and more. This isn’t just another video editing tool, it’s a one-stop AI workspace for all your creative needs. Ready to level up your content? Explore Dreamina Seedance 2.0 and start creating today. 🔗 #dreamina #seedance2 #seedream5 #dreaminatutorial #ai #aitools #aidesign #ecommercedesign #digitalmarketing #startupbusiness

GitHub Projects Community

18,440 views • 4 months ago

currently experimenting with WAN 2.6 I2V on GMI Cloud in this test, I’m comparing two audio workflows and honestly both perform really well. one scene uses audio generated directly from the prompt, while the other uses manually uploaded audio taken from the film 300. visually, both deliver strong motion and solid performance. however, the version with audio coming straight from the prompt feels slightly more refined, camera movement is smoother, transitions flow more naturally and the sync between voice, facial motion, and pacing feels more cohesive. lip sync, especially for Chinese dialogue also comes across a bit cleaner. you can choose single shot for a clean, focused moment or multi shot if you want more cinematic transitions, even when working from just one reference image. one important note: always turn on prompt extension. it makes a noticeable difference in how well the model understands motion, transitions and overall scene flow. both audio approaches are totally usable, but if you’re building dialogue driven or cinematic scenes, starting with audio from the prompt gives WAN 2.6 a bit more context to work with. I’ll be pushing this further with more dynamic camera movement and transitions next. more experiments coming soon ✨ Wan
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currently experimenting with WAN 2.6 I2V on GMI Cloud in this test, I’m comparing two audio workflows and honestly both perform really well. one scene uses audio generated directly from the prompt, while the other uses manually uploaded audio taken from the film 300. visually, both deliver strong motion and solid performance. however, the version with audio coming straight from the prompt feels slightly more refined, camera movement is smoother, transitions flow more naturally and the sync between voice, facial motion, and pacing feels more cohesive. lip sync, especially for Chinese dialogue also comes across a bit cleaner. you can choose single shot for a clean, focused moment or multi shot if you want more cinematic transitions, even when working from just one reference image. one important note: always turn on prompt extension. it makes a noticeable difference in how well the model understands motion, transitions and overall scene flow. both audio approaches are totally usable, but if you’re building dialogue driven or cinematic scenes, starting with audio from the prompt gives WAN 2.6 a bit more context to work with. I’ll be pushing this further with more dynamic camera movement and transitions next. more experiments coming soon ✨ Wan

DStudioproject

96,950 views • 7 months ago

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )
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🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )

Kanika

14,760 views • 3 months ago

Before the week ends, let's acknowledge one of the most INSANE week ever for open AI, with 25+ notable open-weight drops across every modality: 🧠 LLMs → NVIDIA Nemotron 3 Ultra: 550B hybrid Mamba-MoE, only 55B active, 1M context, MMLU 89.1. NVFP4 variant claims ~5x throughput on Blackwell. First openly-weighted 550B hybrid Mamba-Transformer, closing the gap with frontier closed models. → Google Gemma 4 12B: fully open dense any-to-any (text/image/audio/video), 256k context, encoder-free, 140+ languages, AIME 2026 at 77.5. Shipped with a 23-checkpoint QAT wave (mobile ONNX + MLX). Most deployable model of the week. → StepFun Step-3.7-Flash: 198B sparse MoE VLM, ~11B active, SWE-Bench PRO 56.3. Apache 2.0. → Liquid AI LFM2.5-8B-A1B: edge MoE, just 1.5B active, 128k ctx, MATH500 88.8, MLX-ready. Best on-device option this week. → JetBrains Mellum2-12B-A2.5B-Thinking: their first open MoE, near-Qwen3-14B coding at 2.5B active. Apache 2.0. 🎨 Image gen (the surprise of the week) → Ideogram 4: their FIRST-EVER open weights. 9.3B flow-matching DiT trained from scratch. #2 overall behind GPT Image 2, top open-weight model on Design Arena + LMArena. Strongest open checkpoint for text-rich images, full stop. It has taste. Still can't believe this is open weights. 🔊 Audio & Speech (a breakout week for open TTS, 4 labs shipped) → Boson Higgs Audio v3 4B: 102 languages, 21 emotions, singing/whispering/shouting, sub-second TTFA. → RedNote dots.tts: the only fully continuous (no codec) open TTS pipeline, Apache 2.0. → Google Magenta RealTime 2: real-time music gen, <200ms latency, text+audio+MIDI. multimodalart ported it to PyTorch within hours with live ZeroGPU demos. → NVIDIA Nemotron-3.5 ASR: 600M streaming, 17x more concurrent streams vs Parakeet RNNT 1.1B. 👁️ Vision & VLMs → PaddleOCR-VL-1.6: SOTA document parsing at 1B params, Apache 2.0. → Baidu NAVA: 6.3B joint audio-video gen, best-in-class A/V sync, Apache 2.0. 🎬 Video, 3D & World Models → NVIDIA Cosmos3-Super: 64B omnimodal world model coupling action trajectories with video+audio gen, for Physical AI. → JD JoyAI-Echo: up to 5-min multi-shot text-to-video on LTX-2.3. → ByteDance Bernini-R + VAST TripoSplat (single-image-to-3D Gaussian splats, MIT).
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Before the week ends, let's acknowledge one of the most INSANE week ever for open AI, with 25+ notable open-weight drops across every modality: 🧠 LLMs → NVIDIA Nemotron 3 Ultra: 550B hybrid Mamba-MoE, only 55B active, 1M context, MMLU 89.1. NVFP4 variant claims ~5x throughput on Blackwell. First openly-weighted 550B hybrid Mamba-Transformer, closing the gap with frontier closed models. → Google Gemma 4 12B: fully open dense any-to-any (text/image/audio/video), 256k context, encoder-free, 140+ languages, AIME 2026 at 77.5. Shipped with a 23-checkpoint QAT wave (mobile ONNX + MLX). Most deployable model of the week. → StepFun Step-3.7-Flash: 198B sparse MoE VLM, ~11B active, SWE-Bench PRO 56.3. Apache 2.0. → Liquid AI LFM2.5-8B-A1B: edge MoE, just 1.5B active, 128k ctx, MATH500 88.8, MLX-ready. Best on-device option this week. → JetBrains Mellum2-12B-A2.5B-Thinking: their first open MoE, near-Qwen3-14B coding at 2.5B active. Apache 2.0. 🎨 Image gen (the surprise of the week) → Ideogram 4: their FIRST-EVER open weights. 9.3B flow-matching DiT trained from scratch. #2 overall behind GPT Image 2, top open-weight model on Design Arena + LMArena. Strongest open checkpoint for text-rich images, full stop. It has taste. Still can't believe this is open weights. 🔊 Audio & Speech (a breakout week for open TTS, 4 labs shipped) → Boson Higgs Audio v3 4B: 102 languages, 21 emotions, singing/whispering/shouting, sub-second TTFA. → RedNote dots.tts: the only fully continuous (no codec) open TTS pipeline, Apache 2.0. → Google Magenta RealTime 2: real-time music gen, <200ms latency, text+audio+MIDI. multimodalart ported it to PyTorch within hours with live ZeroGPU demos. → NVIDIA Nemotron-3.5 ASR: 600M streaming, 17x more concurrent streams vs Parakeet RNNT 1.1B. 👁️ Vision & VLMs → PaddleOCR-VL-1.6: SOTA document parsing at 1B params, Apache 2.0. → Baidu NAVA: 6.3B joint audio-video gen, best-in-class A/V sync, Apache 2.0. 🎬 Video, 3D & World Models → NVIDIA Cosmos3-Super: 64B omnimodal world model coupling action trajectories with video+audio gen, for Physical AI. → JD JoyAI-Echo: up to 5-min multi-shot text-to-video on LTX-2.3. → ByteDance Bernini-R + VAST TripoSplat (single-image-to-3D Gaussian splats, MIT).

Victor M

540,198 views • 1 month ago

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.
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📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

12,846 views • 1 month ago

Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)
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Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Mike Futia

25,478 views • 5 months ago

I’ve used all the recent GenAI video models extensively & here’s my 2¢: 🎬 Runway Gen3 Alpha - best image quality & motion for text-to-video & embedded words. Great at prompt travel changes over the course of 10 sec. And I’m super bullish on how gen3 will evolve, hopefully adopting the features listed below. Kling - best quality for image-to-video with prompt control, like eating food. Great clip extension that accounts for character (ie walking stride) & camera movement (speed & angle), rather than just using final frame. But it’s limited availability & Chinese native language is limiting. Used for Spider-Man video below (via Midjourney). LumaLabs - best for keyframe start & end control (it can not be overstated how important this is. other services should add it ASAP!) and their high dynamic action movements are really fun. Luma was used in my viral Multiverse of Memes video. PikaLabs - they haven’t gotten as much attention as others lately. But they did update their video model a few weeks ago and it looks great. Also, they are notable for their unique & AWESOME features, like video in-painting & out-painting. My perfect AI video platform would have the following features: 1) Gen3’s quality, prompt control & text embedding. 2) KLing’s image-to-video quality, prompt control & clip extension quality. 3) Luma’s multi-keyframe control & dynamic movement ability. 4) Pika’s inpainting & outpainting ability. And a video-to-video (aka next-gen Runway gen1) could be a game changer, too. It’s an exciting time to be alive 🫶 Who will get there first? 🔉🔉
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I’ve used all the recent GenAI video models extensively & here’s my 2¢: 🎬 Runway Gen3 Alpha - best image quality & motion for text-to-video & embedded words. Great at prompt travel changes over the course of 10 sec. And I’m super bullish on how gen3 will evolve, hopefully adopting the features listed below. Kling - best quality for image-to-video with prompt control, like eating food. Great clip extension that accounts for character (ie walking stride) & camera movement (speed & angle), rather than just using final frame. But it’s limited availability & Chinese native language is limiting. Used for Spider-Man video below (via Midjourney). LumaLabs - best for keyframe start & end control (it can not be overstated how important this is. other services should add it ASAP!) and their high dynamic action movements are really fun. Luma was used in my viral Multiverse of Memes video. PikaLabs - they haven’t gotten as much attention as others lately. But they did update their video model a few weeks ago and it looks great. Also, they are notable for their unique & AWESOME features, like video in-painting & out-painting. My perfect AI video platform would have the following features: 1) Gen3’s quality, prompt control & text embedding. 2) KLing’s image-to-video quality, prompt control & clip extension quality. 3) Luma’s multi-keyframe control & dynamic movement ability. 4) Pika’s inpainting & outpainting ability. And a video-to-video (aka next-gen Runway gen1) could be a game changer, too. It’s an exciting time to be alive 🫶 Who will get there first? 🔉🔉

Blaine Brown

26,535 views • 2 years ago

Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:
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Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:

Bilawal Sidhu

44,822 views • 2 years ago

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)
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Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Ahad Shams

11,067 views • 1 month ago

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

112,114 views • 17 days ago

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!
0:14

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 views • 1 month ago

Made this cinematic AI video in minutes using Getvivix Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:
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Made this cinematic AI video in minutes using Getvivix Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:

Zoraiz Ai

10,748 views • 1 month ago

Real or AI? AI stadium broadcast trend 💛 💙 • Create the video here: 🔗[ ] - How it works? 1. Upload your photo to ChatGPT with this prompt: [PHOTO PROMPT] Realistic sports broadcast screenshot-style documentary photo set in the spectator stands of a [WRITE YOUR TEAM HERE] football match. Analyze the uploaded image and show the person sitting in the stadium seats. The person has delicate facial features and a surprised yet focused expression while looking toward the field. The person is wearing a [WRITE YOUR TEAM HERE] jersey. OUTPUT: ratio: 16:9 broadcast frame, realistic TV capture quality. 2. Open the link above → select “Text to Video” → upload the generated image + use this prompt: [VIDEO PROMPT] Dimage = character identity reference only (face, hairstyle, proportions).Preserve exact face, hairstyle, skin texture, and identity. Do NOT stylize or beautify.Output: single continuous live sports broadcast shot, 4-5s, 16:9, 1080p, no cuts. SUBJECT:A young woman based on Image, sitting in a [WRITE YOUR TEAM HERE] football stadium audience.Hands resting naturally on her lap or lightly placed on the seat.Neutral, slightly distant expression.Natural breathing, minimal movement. ENVIRONMENT: [WRITE YOUR TEAM HERE] stadium crowd during live match.Plastic seats, fans around her wearing [WRITE YOUR TEAM HERE] jerseys. Background slightly out of focus.Realistic stadium lighting - day or night.Slight haze from broadcast compression. MOOD:Unstaged, candid, real broadcast moment No cinematic drama. Pure live TV capture. CAMERA:Telephoto broadcast lens (120-150mm).Long-distance zoom from upper stands camera.Strong compression, shallow depth of field.Eye-level, very slight upward tilt.Subtle micro-shake from broadcast stabilization. ACTION (4-5s):[0-2s] She sits still, blinks once. Hands resting naturally.[2-4s] Subtle weight shift, naturally adjusting posture. Minimal body movement.[4-5s] Small hand reposition on lap or seat. Slight head turn toward the field._ DETAILS:No posing. No eye contact with camera. Skin texture realistic, no smoothing or beautification. Slight motion blur on background crowd.Faint broadcast scoreboard UI visible in corner.
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Real or AI? AI stadium broadcast trend 💛 💙 • Create the video here: 🔗[ ] - How it works? 1. Upload your photo to ChatGPT with this prompt: [PHOTO PROMPT] Realistic sports broadcast screenshot-style documentary photo set in the spectator stands of a [WRITE YOUR TEAM HERE] football match. Analyze the uploaded image and show the person sitting in the stadium seats. The person has delicate facial features and a surprised yet focused expression while looking toward the field. The person is wearing a [WRITE YOUR TEAM HERE] jersey. OUTPUT: ratio: 16:9 broadcast frame, realistic TV capture quality. 2. Open the link above → select “Text to Video” → upload the generated image + use this prompt: [VIDEO PROMPT] Dimage = character identity reference only (face, hairstyle, proportions).Preserve exact face, hairstyle, skin texture, and identity. Do NOT stylize or beautify.Output: single continuous live sports broadcast shot, 4-5s, 16:9, 1080p, no cuts. SUBJECT:A young woman based on Image, sitting in a [WRITE YOUR TEAM HERE] football stadium audience.Hands resting naturally on her lap or lightly placed on the seat.Neutral, slightly distant expression.Natural breathing, minimal movement. ENVIRONMENT: [WRITE YOUR TEAM HERE] stadium crowd during live match.Plastic seats, fans around her wearing [WRITE YOUR TEAM HERE] jerseys. Background slightly out of focus.Realistic stadium lighting - day or night.Slight haze from broadcast compression. MOOD:Unstaged, candid, real broadcast moment No cinematic drama. Pure live TV capture. CAMERA:Telephoto broadcast lens (120-150mm).Long-distance zoom from upper stands camera.Strong compression, shallow depth of field.Eye-level, very slight upward tilt.Subtle micro-shake from broadcast stabilization. ACTION (4-5s):[0-2s] She sits still, blinks once. Hands resting naturally.[2-4s] Subtle weight shift, naturally adjusting posture. Minimal body movement.[4-5s] Small hand reposition on lap or seat. Slight head turn toward the field._ DETAILS:No posing. No eye contact with camera. Skin texture realistic, no smoothing or beautification. Slight motion blur on background crowd.Faint broadcast scoreboard UI visible in corner.

Zaylee

26,495 views • 2 months ago

A FULL ANIME FIGHT SCENE GENERATED WITH AI. AND THE PROMPT IS PUBLIC. Higgsfield just open-sourced its Originals: their flagship in-house productions are now fully transparent. For every video you can see: → the full prompt → every reference (image and audio) → the exact generation settings: model, quality, resolution This clip has a creature transformation, two-character fight choreography, slow motion, even pink blood to dodge the gore. All described in a single prompt you can copy, reproduce or remix as is. Full prompt: "Girll with blue hair and guy with black hair enters empty classroom at dusk, red-orange sky bleeding through tall windows, desks and chairs overturned and scattered with loose papers on the floor; the black-bob girl in a white sailor uniform with blue collar sits collapsed against the wall beneath the blackboard, knees drawn up, sweat soaking her bangs, shoulders convulsing in short spasms, one hand pressed to her stomach, breath ragged and wet. Blue-haired girl in a navy school blazer and pleated skirt enters first through the sliding door, kusarigama scythe held low at her side, blade catching the red window-light; black-haired boy in an open-collar white shirt and loosened tie follows a beat behind, war hammer resting on his shoulder, smirk fading as he sees the sick girl. Camera pushes in low and fast toward the collapsed girl, handheld micro-shake, then whip-pans to the two arrivals freezing in the doorway. The sick girl's spine arches unnaturally, a wet tearing sound as her skin splits along the shoulder blades, bone spurs punching outward, extra eyelids blistering open across her collarbone and cheeks — camera holds in a static wide as the transformation escalates, cutting to tight inserts of individual eyes opening one by one along her growing arms, her school uniform shredding as the body doubles then triples in mass, clawed feet cracking the floorboards, her head deforming into a horned, skull-white cranium ringed with curved fangs, one massive central eye where her face used to be, remnants of the blue collar-ribbon still hanging from a warped throat. She rises to full monster height, towering over the desks, guttural roar rattling the windows. Blue-haired girl plants her feet and swings the scythe up into guard as the boy drops the hammer off his shoulder into a two-handed grip; camera does a fast 180-degree orbit around both of them as they charge. Dutch-angle low shot as the boy closes distance first, hammer swinging in a wide horizontal arc into the monster's leg — impact throws him sideways through a row of desks, wood splintering, into the side wall, plaster cracking around his body, hard cut to his grunt and a slow recovery push off the wall. Blue-haired girl vaults over the wreckage, scythe trailing a whip of motion blur, camera speed-ramping into slow motion as the curved blade connects across the monster's forearm — hot pink ichor sprays in thick arcing ribbons instead of red blood, droplets suspended mid-air in the slowed frame, blade sound a wet metallic tear. Monster backhands her mid-recovery, camera whip-pans to follow her body slamming into the blackboard, chalk dust exploding outward, board cracking down the middle. Rapid cross-cutting: low hero-angle on the boy re-entering with a rising hammer strike into the monster's exposed ribcage-like plating, close slow-mo insert on the point of impact as bone-white shards and pink fluid burst outward; over-the-shoulder shot from the monster's height looking down as both fighters flank it from opposite sides. Girl's scythe hooks into the monster's shoulder joint, camera locked tight on her straining grip and gritted teeth as she wrenches it downward, boy's hammer following through into the same joint a half-second later — impact sound layered, thick and final, monster's roar breaking into a low collapsing groan as its mass folds inward and it drops to its knees then face-first into the floor in a slow, weighty fall, camera pulling back and up into a wide static shot of the wrecked classroom settling into stillness. Both fighters stand over the fallen mass, weapons lowering, chests heaving, camera slowly circling them at floor level; blue-haired girl wipes pink fluid off her cheek with the back of her wrist and starts laughing between breaths, boy lets the hammer head drop to the floor with a thud and laughs too, leaning against a desk, both grinning, exhausted, shoulder-checking each other, red dusk light still pouring through the cracked windows. Ambient sound throughout: creaking wood, scattering papers, cracking plaster, wet impact foley, labored breathing, the monster's guttural vocalizations, ending on genuine breathless laughter. Render risk: full-body transformation sequence may render as a static morph or skip frames — fallback to hard-cutting between three discrete transformation stages rather than one continuous morph if the engine smooths it into mush. Risk: pink blood may drift toward red under default color grading — reinforce with explicit magenta-pink hue callouts at each blood beat. Risk: multi-character wide fight choreography may merge the two fighters' silhouettes in wide shots — favor alternating single-subject framing over simultaneous two-shot action if clarity fails." Made with Seedance 2.0 on Higgsfield AI Full open-sourced prompts & assets below👇
0:11

A FULL ANIME FIGHT SCENE GENERATED WITH AI. AND THE PROMPT IS PUBLIC. Higgsfield just open-sourced its Originals: their flagship in-house productions are now fully transparent. For every video you can see: → the full prompt → every reference (image and audio) → the exact generation settings: model, quality, resolution This clip has a creature transformation, two-character fight choreography, slow motion, even pink blood to dodge the gore. All described in a single prompt you can copy, reproduce or remix as is. Full prompt: "Girll with blue hair and guy with black hair enters empty classroom at dusk, red-orange sky bleeding through tall windows, desks and chairs overturned and scattered with loose papers on the floor; the black-bob girl in a white sailor uniform with blue collar sits collapsed against the wall beneath the blackboard, knees drawn up, sweat soaking her bangs, shoulders convulsing in short spasms, one hand pressed to her stomach, breath ragged and wet. Blue-haired girl in a navy school blazer and pleated skirt enters first through the sliding door, kusarigama scythe held low at her side, blade catching the red window-light; black-haired boy in an open-collar white shirt and loosened tie follows a beat behind, war hammer resting on his shoulder, smirk fading as he sees the sick girl. Camera pushes in low and fast toward the collapsed girl, handheld micro-shake, then whip-pans to the two arrivals freezing in the doorway. The sick girl's spine arches unnaturally, a wet tearing sound as her skin splits along the shoulder blades, bone spurs punching outward, extra eyelids blistering open across her collarbone and cheeks — camera holds in a static wide as the transformation escalates, cutting to tight inserts of individual eyes opening one by one along her growing arms, her school uniform shredding as the body doubles then triples in mass, clawed feet cracking the floorboards, her head deforming into a horned, skull-white cranium ringed with curved fangs, one massive central eye where her face used to be, remnants of the blue collar-ribbon still hanging from a warped throat. She rises to full monster height, towering over the desks, guttural roar rattling the windows. Blue-haired girl plants her feet and swings the scythe up into guard as the boy drops the hammer off his shoulder into a two-handed grip; camera does a fast 180-degree orbit around both of them as they charge. Dutch-angle low shot as the boy closes distance first, hammer swinging in a wide horizontal arc into the monster's leg — impact throws him sideways through a row of desks, wood splintering, into the side wall, plaster cracking around his body, hard cut to his grunt and a slow recovery push off the wall. Blue-haired girl vaults over the wreckage, scythe trailing a whip of motion blur, camera speed-ramping into slow motion as the curved blade connects across the monster's forearm — hot pink ichor sprays in thick arcing ribbons instead of red blood, droplets suspended mid-air in the slowed frame, blade sound a wet metallic tear. Monster backhands her mid-recovery, camera whip-pans to follow her body slamming into the blackboard, chalk dust exploding outward, board cracking down the middle. Rapid cross-cutting: low hero-angle on the boy re-entering with a rising hammer strike into the monster's exposed ribcage-like plating, close slow-mo insert on the point of impact as bone-white shards and pink fluid burst outward; over-the-shoulder shot from the monster's height looking down as both fighters flank it from opposite sides. Girl's scythe hooks into the monster's shoulder joint, camera locked tight on her straining grip and gritted teeth as she wrenches it downward, boy's hammer following through into the same joint a half-second later — impact sound layered, thick and final, monster's roar breaking into a low collapsing groan as its mass folds inward and it drops to its knees then face-first into the floor in a slow, weighty fall, camera pulling back and up into a wide static shot of the wrecked classroom settling into stillness. Both fighters stand over the fallen mass, weapons lowering, chests heaving, camera slowly circling them at floor level; blue-haired girl wipes pink fluid off her cheek with the back of her wrist and starts laughing between breaths, boy lets the hammer head drop to the floor with a thud and laughs too, leaning against a desk, both grinning, exhausted, shoulder-checking each other, red dusk light still pouring through the cracked windows. Ambient sound throughout: creaking wood, scattering papers, cracking plaster, wet impact foley, labored breathing, the monster's guttural vocalizations, ending on genuine breathless laughter. Render risk: full-body transformation sequence may render as a static morph or skip frames — fallback to hard-cutting between three discrete transformation stages rather than one continuous morph if the engine smooths it into mush. Risk: pink blood may drift toward red under default color grading — reinforce with explicit magenta-pink hue callouts at each blood beat. Risk: multi-character wide fight choreography may merge the two fighters' silhouettes in wide shots — favor alternating single-subject framing over simultaneous two-shot action if clarity fails." Made with Seedance 2.0 on Higgsfield AI Full open-sourced prompts & assets below👇

gus

100,376 views • 16 days ago

This guy cracked the code on AI-powered fashion ecommerce using synthetic face technology and now pulls $50,000 to $150,000 per month from two Shopify stores without paying a single real model. He got tired of watching DTC fashion brands burn $20,000 monthly on photoshoots while their competitors tested 40 product angles in the same timeframe, so he built a system that generates hyperrealistic fashion content using his gaming PC and real-time AI masks instead of studios, contracts, or casting calls. His monthly profit hit $150,000 last month from just 2 stores and organic TikTok traffic, while traditional fashion brands cap out at $30K after paying models $400 to $800 per shoot and studio rentals of $200 to $500 per session. Here is the exact breakdown: → Real-time synthetic face technology becomes the only tool you need, but most people butcher the setup by skipping motion sync calibration in the first 30 seconds → Product selection comes first, and if you mess this up nothing saves it. Stick to women's accessories (bags, sunglasses, jewelry) because that is where organic TikTok engagement lives → Avatar casting is not random. You build one consistent AI face that repeats across all content so your audience recognizes the "model" and trusts the brand continuity → You are picking who your customer projects onto, not who looks expensive. That is your positioning baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys watch time in 4 seconds → You mirror your own gestures through webcam: wave, chin tap, finger point, shoulder dance. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 94 percent skip: same outfit base, multiple product swaps, one recording session. No re-shooting, no model schedules, no usage rights negotiations → The system generates 3 to 5 TikToks before lunch, while traditional brands test 2 per week and wonder why their conversion rates are stuck at 0.8 percent The economics are stupid: each video costs him $0 in talent fees, pulls 1.5 million views organically, converts at 0.03 percent into 450 orders at $45 to $60 retail with $30 to $45 margin per sale. That is $15,750 profit per viral video, while fashion brands pay $1,200 per shoot and net $3,000 after ads. The key move nobody talks about: you cannot skip the motion synchronization test. If you generate the AI face without mirroring your own natural gestures first, the avatar moves like a mannequin. The blinks lag. The smile timing breaks. The whole thing screams "synthetic face technology" and your hook rate dies at 1.1 seconds. His system records him doing the exact dance trend first, so the AI mask inherits human timing, natural head tilts, and spontaneous energy that reads as a real creator showing off a product find, not a rendered advertisement. One accessories store generated 10 variants of the same handbag reveal in 18 minutes with different outfits, different backgrounds, different trend audios, and found the winner in 72 hours without spending $6,000 on influencer gifting. They were previously paying $800 per UGC creator and burning $4,800 per week on content that plateaued at 40K views. Now they spend $0 for 10 variants and their cost per acquisition dropped from $62 to $18. UGC agencies now panic because their entire margin was built on talent scarcity, and this removes the human bottleneck. The outfit changes between clips like a wardrobe filter. The lighting matches bedroom setups. The hand gestures sync with beat drops. No casting call. No model release. No location permits. Just a webcamera, a real-time AI mask, and the discipline to batch-test product angles before you commit ad spend to one creative.
0:13

This guy cracked the code on AI-powered fashion ecommerce using synthetic face technology and now pulls $50,000 to $150,000 per month from two Shopify stores without paying a single real model. He got tired of watching DTC fashion brands burn $20,000 monthly on photoshoots while their competitors tested 40 product angles in the same timeframe, so he built a system that generates hyperrealistic fashion content using his gaming PC and real-time AI masks instead of studios, contracts, or casting calls. His monthly profit hit $150,000 last month from just 2 stores and organic TikTok traffic, while traditional fashion brands cap out at $30K after paying models $400 to $800 per shoot and studio rentals of $200 to $500 per session. Here is the exact breakdown: → Real-time synthetic face technology becomes the only tool you need, but most people butcher the setup by skipping motion sync calibration in the first 30 seconds → Product selection comes first, and if you mess this up nothing saves it. Stick to women's accessories (bags, sunglasses, jewelry) because that is where organic TikTok engagement lives → Avatar casting is not random. You build one consistent AI face that repeats across all content so your audience recognizes the "model" and trusts the brand continuity → You are picking who your customer projects onto, not who looks expensive. That is your positioning baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys watch time in 4 seconds → You mirror your own gestures through webcam: wave, chin tap, finger point, shoulder dance. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 94 percent skip: same outfit base, multiple product swaps, one recording session. No re-shooting, no model schedules, no usage rights negotiations → The system generates 3 to 5 TikToks before lunch, while traditional brands test 2 per week and wonder why their conversion rates are stuck at 0.8 percent The economics are stupid: each video costs him $0 in talent fees, pulls 1.5 million views organically, converts at 0.03 percent into 450 orders at $45 to $60 retail with $30 to $45 margin per sale. That is $15,750 profit per viral video, while fashion brands pay $1,200 per shoot and net $3,000 after ads. The key move nobody talks about: you cannot skip the motion synchronization test. If you generate the AI face without mirroring your own natural gestures first, the avatar moves like a mannequin. The blinks lag. The smile timing breaks. The whole thing screams "synthetic face technology" and your hook rate dies at 1.1 seconds. His system records him doing the exact dance trend first, so the AI mask inherits human timing, natural head tilts, and spontaneous energy that reads as a real creator showing off a product find, not a rendered advertisement. One accessories store generated 10 variants of the same handbag reveal in 18 minutes with different outfits, different backgrounds, different trend audios, and found the winner in 72 hours without spending $6,000 on influencer gifting. They were previously paying $800 per UGC creator and burning $4,800 per week on content that plateaued at 40K views. Now they spend $0 for 10 variants and their cost per acquisition dropped from $62 to $18. UGC agencies now panic because their entire margin was built on talent scarcity, and this removes the human bottleneck. The outfit changes between clips like a wardrobe filter. The lighting matches bedroom setups. The hand gestures sync with beat drops. No casting call. No model release. No location permits. Just a webcamera, a real-time AI mask, and the discipline to batch-test product angles before you commit ad spend to one creative.

Shade

20,190 views • 2 months ago

This guy cracked the code on AI girlfriend monetization using real-time technology and now pulls $76,000 per month from one Instagram profile without ever showing his real face or hiring an actual model. He got tired of watching creators split 80 percent of revenue with agencies while their competitors ran 24/7 chat operations with zero burnout, so he built a system that generates hyperrealistic AI influencer content using motion capture and synthetic face generation instead of photographers, makeup artists, or Miami beach rentals. His monthly profit hit $76,455 last month from just 90.4K followers and organic short-form traffic, while traditional creators cap out at $15K after paying 40 percent platform fees and $2,000 monthly for content production teams. Here is the exact breakdown: → Real-time face swap technology becomes the only tool you need, but most people butcher the setup by skipping gesture synchronization in the first 10 seconds → Character design comes first, and if you mess this up nothing saves it. Stick to approachable features (freckles, natural makeup, warm smile) because that is where parasocial engagement lives → Profile building is not random. You craft one consistent AI persona that repeats across all content so your audience recognizes the girl → You are picking who your subscriber projects onto, not who looks unattainable. That is your retention baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys engagement in 3 seconds → You mirror your own gestures through webcam: confused shrug, hand raise, lean-in shock, peace sign wave. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 91 percent skip: same room setup, multiple emotion sequences, one recording session. → The system generates 7 to 10 TikToks before dinner, while traditional creators test 3 per week and wonder why their conversion rates are stuck at 0.4 percent The economics are stupid: each video costs him $0 in talent fees, pulls 2 million views organically, converts at 2 percent into 1,800 clicks to private platforms at $10 to $15 subscription with $40 to $60 backend PPV per fan. That is $76,455 profit per month, while real creators pay $5,000 for production and net $22,000 after platform cuts. The key move nobody talks about: you cannot skip the natural gesture library. If you generate the AI face without mirroring your own spontaneous reactions first, the avatar moves like a CGI render. The eye contact breaks. The smile timing lags. The whole thing screams and your retention dies at 2.1 seconds. His system records him doing the exact confusion-to-delight emotional arc first, so the AI mask inherits human timing, natural eyebrow raises, and spontaneous energy that reads as a real girl reacting to comments, not a scripted advertisement. One Instagram profile generated 12 variants of the same "how I afford this lifestyle" hook in 40 minutes with different outfits, different lighting setups, different trending audios, and found the winner in 96 hours without spending $8,000 on influencer collaborations. They were previously paying $1,200 per UGC creator and burning $6,400 per week on content that plateaued at 60K views. Now they spend $0 for 12 variants and their cost per subscriber dropped from $48 to $11. Agencies now panic because their entire margin was built on model exclusivity, and this removes the human dependency. The outfit changes between clips like a wardrobe swap filter. The lighting matches bedroom authenticity. The hand gestures sync with emotional beats. No casting call. No model contract. No location scouting. Just a webcamera, a real-time face swap AI, and the discipline to batch-test emotional hooks before you commit traffic spend to one persona.
0:10

This guy cracked the code on AI girlfriend monetization using real-time technology and now pulls $76,000 per month from one Instagram profile without ever showing his real face or hiring an actual model. He got tired of watching creators split 80 percent of revenue with agencies while their competitors ran 24/7 chat operations with zero burnout, so he built a system that generates hyperrealistic AI influencer content using motion capture and synthetic face generation instead of photographers, makeup artists, or Miami beach rentals. His monthly profit hit $76,455 last month from just 90.4K followers and organic short-form traffic, while traditional creators cap out at $15K after paying 40 percent platform fees and $2,000 monthly for content production teams. Here is the exact breakdown: → Real-time face swap technology becomes the only tool you need, but most people butcher the setup by skipping gesture synchronization in the first 10 seconds → Character design comes first, and if you mess this up nothing saves it. Stick to approachable features (freckles, natural makeup, warm smile) because that is where parasocial engagement lives → Profile building is not random. You craft one consistent AI persona that repeats across all content so your audience recognizes the girl → You are picking who your subscriber projects onto, not who looks unattainable. That is your retention baked into the face → Motion capture runs before generation, and this is what kills the uncanny valley effect that destroys engagement in 3 seconds → You mirror your own gestures through webcam: confused shrug, hand raise, lean-in shock, peace sign wave. The AI mask tracks every micro-movement and applies it to the generated face in real time → Batching is the move 91 percent skip: same room setup, multiple emotion sequences, one recording session. → The system generates 7 to 10 TikToks before dinner, while traditional creators test 3 per week and wonder why their conversion rates are stuck at 0.4 percent The economics are stupid: each video costs him $0 in talent fees, pulls 2 million views organically, converts at 2 percent into 1,800 clicks to private platforms at $10 to $15 subscription with $40 to $60 backend PPV per fan. That is $76,455 profit per month, while real creators pay $5,000 for production and net $22,000 after platform cuts. The key move nobody talks about: you cannot skip the natural gesture library. If you generate the AI face without mirroring your own spontaneous reactions first, the avatar moves like a CGI render. The eye contact breaks. The smile timing lags. The whole thing screams and your retention dies at 2.1 seconds. His system records him doing the exact confusion-to-delight emotional arc first, so the AI mask inherits human timing, natural eyebrow raises, and spontaneous energy that reads as a real girl reacting to comments, not a scripted advertisement. One Instagram profile generated 12 variants of the same "how I afford this lifestyle" hook in 40 minutes with different outfits, different lighting setups, different trending audios, and found the winner in 96 hours without spending $8,000 on influencer collaborations. They were previously paying $1,200 per UGC creator and burning $6,400 per week on content that plateaued at 60K views. Now they spend $0 for 12 variants and their cost per subscriber dropped from $48 to $11. Agencies now panic because their entire margin was built on model exclusivity, and this removes the human dependency. The outfit changes between clips like a wardrobe swap filter. The lighting matches bedroom authenticity. The hand gestures sync with emotional beats. No casting call. No model contract. No location scouting. Just a webcamera, a real-time face swap AI, and the discipline to batch-test emotional hooks before you commit traffic spend to one persona.

Shade

20,831 views • 2 months ago

I would like to explain the latest batch of viral videos I'm working on to the bemused brainrot-curious reader who is not familiar with "the culture". Why are these characters, mixed with this song, going viral? It's all about connecting infinite referential mirrors. What makes this video interesting are not its individual parts but the signifier links it draws. Let's look at the individual parts: ONE: The song is a Brazilian funk or "pancadão" song called MC Lan e MC WM - Sua Amiga Vou Pegar, these days part of what's broadly referred as Brazilian phonk or just phonk (not to be confused with the original phonk, a Memphis-derived genre from the early 2010s built around chopped Three 6 Mafia samples, cowbells and lo-fi tape hiss and etc. The Brazilian version comes an entirely different lineage and got its name adapted from “funk” to “phonk” exclusively because the names sounded similar. It has a similarly menacing posture but swaps the rap cadence for funk's 4/4 with kicks on 1 and 3 rhythm and a much heavier, distorted 808 synth sound). Phonk is often used for its exaggerated reverb feeling bass lines to signify power, style or simply "aura", which you can take as a shorthand for poise, coolness, being de-bon-air and a general detached positive feeling of high status. Aura. Because most users cannot understand the Portuguese lyrics (which are often quite vulgar and sexual), the singing takes the characteristic of a chant, something to be appreciated entirely for its sound, texture and gravitas. The vocals are just another instrument where you can appreciate the menace and swagger of the delivery directly without the cognitive friction of meaning. Non-Portuguese-speaking audiences are not missing anything they were supposed to get, they get “the vibe” that matters, which is not lyrical. These songs are often paired with (male) characters that are taken to display these traits like American Psycho's Patrick Bateman (yes, yes I know that’s the opposite of what you should feel about the character), Peaky Blinder's Thomas Shelby and a menagerie of anime characters like Satoru Gojo (Jujutsu Kaisen), Yujiro Hanma (Baki) and Goku and, really, any male character that is just a little bit cool. TWO: The man in the suit is a minor Family Guy character called Tom Tucker. The reference comes from a scene where Meg sees him walking through her school and says "It's Tom Tucker from the news!” We then cut to her POV, where he is walking in slow motion with soft romantic music swelling and birds chirping, the whole love-at-first-sight trope. Then a camera crew member off-screen yells "hurry up Mr. Tucker," and we get to see he is not walking in slow motion because Meg is infatuated, he is just walking that slowly in real life. Only the music and the birds were in her head. The gag is built on the viewer recognizing the romantic-slow-motion trope, briefly accepting it as the scene's reality, and then being shown that we (and Meg) projected the trope onto what is actually just a man walking very slowly. HA! The original gag is already about projection: a neutral image (slow walk) being assigned an external meaning (romance) by a viewer's pattern-recognition. This is what makes the edit-culture appropriation work so well. The clip got stripped of its context, paired with phonk and text overlays (AURA or “Me and the boys going to detention”), and retroactively assigned a new meaning, only this time it’s the cinematic nonchalant walk, the slow deliberate gait that signifies a man who knows he's the most important thing in the frame (ta la any 1980s Schwazerneggerian action movie hero walking away from an explosion without looking back, every yakuza boss entering a room, every western gunslinger approaching the duel). The edit is ostensibly projecting a trope onto a neutral image. The first projection was romance; the second projection is aura. Family Guy clips and gifs are easy to access and repost, which makes it a readily available and easy to use building block. The show has, through sheer volume of output and over two decades of YouTube and cable TV saturation, become a kind of public-domain visual library, a default vocabulary that any editor can pull from knowing the audience will recognize the source without having to be told, and we can just keep loading meaning onto it. THREE: The character in the background is Tom, from Tom and Jerry, doing a pose made famous by an iShowSpeed fan who encountered him during a livestream. By quickly and correctly identifying Speed by his full legal name ("Darren Jason Watkins Jr"), she showcased herself to be a true fan, which he responded to with his characteristic exaggerated reactions. The pose the girl hit, with the knowing look to the camera, produced a perfect “aura moment” complete commitment, zero irony, the unshakeable conviction that what she was doing was the coolest possible thing to do. As a result, the clip then got endlessly edited with "aura 🥶🥶🥶" captions to canonize it. Aura, in this lexicon, is not granted by the universe; it is summoned by the person's own belief that they have it and by displaying the correct attitude. Tom is also dressed as the previously mentioned Thomas Shelby from Peaky Blinders, which is itself a double signifier. The name match (“Thomas”, get it?) and the suit-and-flat-cap costume turn the cartoon cat into a stand-in for the perhaps most used "high-aura" male character of the past decade, the brooding gangster patriarch whose every cigarette drag has been set to phonk, cinematic scores and electronic music a thousand times over. On top of that, he is made entirely out of chrome, a popular trope of asking ChatGPT (one of the few AI tools people have easy and broad access to) to render things out of very high quality materials to indicate "rarity" or "status" like diamonds, platinum and etc. A sign that itself descends from a longer lineage of in-game cosmetic rarity tiers (League of Legends, MMOs, various skin economy freemium game, the Fortnite battle pass, the Pokémon shiny, dacha games and etc) where material finish is the visual shorthand of value. So "chrome" or "platinum" Tom on top of all previous signifiers signals a “maximized” or “maxxd” version. The image is suppose to invoke the superlative highest possible tier, rarest-drop, legendary-rarity version of aura, the way a kid in a playground would describe their dad as not just strong but the strongest in the world. FOUR: Finally, the background black hole calls back to the original Tom image, where he is surrounded by the universe itself, having ascended. The character has transcended the diegetic frame of his own cartoon and now exists at a cosmological scale, with the black hole standing in for the kind of unmotivated, vibes-based "cosmic" imagery that has become the default background for any video trying to signify that something Big is happening (the same visual motif that has powered comic book characters, anime transformations, video game power ups and anything wants to feel grandiose or “epic” without specifying what about). The black hole means significance in the abstract. At this point I think you understand the mechanism at play here. None of these references resolve to a stable meaning on their own. Tom Tucker is “cool” only in the very short context in which his image served as a substrate; he was convenient footage to pair with a song, and the absurdity of doing an "aura edit" on such a minor, strange character scene makes it all funnier and easier to share. Tom-the-cat is doing the aura pose > the aura pose comes from the iShowSpeed girl > the iShowSpeed girl was cool because she correctly played her part in an established bit of a large streamer with the correct timing and theatrical flair > the bit was cool because it was a shared convention unified by a popular central streamer figure > the convention existed because phonk edits had already trained this exact scenario to be read as confidence-plus-detachment as aura > the chrome finish points to AI image generation quirks > the AI image generation style can be mapped to gaming visual rarity shorthands; the gaming rarity tiers point to a much older logic of precious-metal-as-status. Each step on the referential chain is propped by the one behind it, and the one behind it is propped up by the one behind that, so on and so forth. There is no natural endpoint, the entire structure functions more akin to a network than a linked list. If you stop at any single point and ask "but why is particular signifier cool or funny or interesting”, the answer is always "because of the thing behind it.” It’s hyper-citation, Here, what matters is the structure of the whole rather than the content. This is structure is what I mean by infinite referential mirrors. The rate at which a concept is referencing, remixing and calling back to another is what’s interesting. In other words, It’s the velocity that matters. The chain of recognitions, each "I get that reference," and the cumulative effect of getting six references stacked on top of each other a short span of time gives you the feeling that you are participating in something dense and alive, because it allows you to recognize the shared meme ecosystem of the platform that you are participating in, even if only a glimpse of it. You are inside the culture rather than outside it. The brainrot-curious reader who watches this video and feels nothing, has “failed” to understand the joke because they are outside the hall of mirrors I am describing. You can only get the magic if you step in and start counting the reflections: the song, the suit, the cat, the chrome, the black hole, the transitions the video uses. You are looking at connected parts of this network of symbols and at the speed at which one image hands you off to the next. The entire thirteen-second clip is functioning as a single compressed referential payload that decompresses in the viewer's head into a small private essay exactly like this one. The video allows you to recognize yourself as someone capable of decoding it, and that recognition is the reward. That’s why media like this goes viral.
0:13

I would like to explain the latest batch of viral videos I'm working on to the bemused brainrot-curious reader who is not familiar with "the culture". Why are these characters, mixed with this song, going viral? It's all about connecting infinite referential mirrors. What makes this video interesting are not its individual parts but the signifier links it draws. Let's look at the individual parts: ONE: The song is a Brazilian funk or "pancadão" song called MC Lan e MC WM - Sua Amiga Vou Pegar, these days part of what's broadly referred as Brazilian phonk or just phonk (not to be confused with the original phonk, a Memphis-derived genre from the early 2010s built around chopped Three 6 Mafia samples, cowbells and lo-fi tape hiss and etc. The Brazilian version comes an entirely different lineage and got its name adapted from “funk” to “phonk” exclusively because the names sounded similar. It has a similarly menacing posture but swaps the rap cadence for funk's 4/4 with kicks on 1 and 3 rhythm and a much heavier, distorted 808 synth sound). Phonk is often used for its exaggerated reverb feeling bass lines to signify power, style or simply "aura", which you can take as a shorthand for poise, coolness, being de-bon-air and a general detached positive feeling of high status. Aura. Because most users cannot understand the Portuguese lyrics (which are often quite vulgar and sexual), the singing takes the characteristic of a chant, something to be appreciated entirely for its sound, texture and gravitas. The vocals are just another instrument where you can appreciate the menace and swagger of the delivery directly without the cognitive friction of meaning. Non-Portuguese-speaking audiences are not missing anything they were supposed to get, they get “the vibe” that matters, which is not lyrical. These songs are often paired with (male) characters that are taken to display these traits like American Psycho's Patrick Bateman (yes, yes I know that’s the opposite of what you should feel about the character), Peaky Blinder's Thomas Shelby and a menagerie of anime characters like Satoru Gojo (Jujutsu Kaisen), Yujiro Hanma (Baki) and Goku and, really, any male character that is just a little bit cool. TWO: The man in the suit is a minor Family Guy character called Tom Tucker. The reference comes from a scene where Meg sees him walking through her school and says "It's Tom Tucker from the news!” We then cut to her POV, where he is walking in slow motion with soft romantic music swelling and birds chirping, the whole love-at-first-sight trope. Then a camera crew member off-screen yells "hurry up Mr. Tucker," and we get to see he is not walking in slow motion because Meg is infatuated, he is just walking that slowly in real life. Only the music and the birds were in her head. The gag is built on the viewer recognizing the romantic-slow-motion trope, briefly accepting it as the scene's reality, and then being shown that we (and Meg) projected the trope onto what is actually just a man walking very slowly. HA! The original gag is already about projection: a neutral image (slow walk) being assigned an external meaning (romance) by a viewer's pattern-recognition. This is what makes the edit-culture appropriation work so well. The clip got stripped of its context, paired with phonk and text overlays (AURA or “Me and the boys going to detention”), and retroactively assigned a new meaning, only this time it’s the cinematic nonchalant walk, the slow deliberate gait that signifies a man who knows he's the most important thing in the frame (ta la any 1980s Schwazerneggerian action movie hero walking away from an explosion without looking back, every yakuza boss entering a room, every western gunslinger approaching the duel). The edit is ostensibly projecting a trope onto a neutral image. The first projection was romance; the second projection is aura. Family Guy clips and gifs are easy to access and repost, which makes it a readily available and easy to use building block. The show has, through sheer volume of output and over two decades of YouTube and cable TV saturation, become a kind of public-domain visual library, a default vocabulary that any editor can pull from knowing the audience will recognize the source without having to be told, and we can just keep loading meaning onto it. THREE: The character in the background is Tom, from Tom and Jerry, doing a pose made famous by an iShowSpeed fan who encountered him during a livestream. By quickly and correctly identifying Speed by his full legal name ("Darren Jason Watkins Jr"), she showcased herself to be a true fan, which he responded to with his characteristic exaggerated reactions. The pose the girl hit, with the knowing look to the camera, produced a perfect “aura moment” complete commitment, zero irony, the unshakeable conviction that what she was doing was the coolest possible thing to do. As a result, the clip then got endlessly edited with "aura 🥶🥶🥶" captions to canonize it. Aura, in this lexicon, is not granted by the universe; it is summoned by the person's own belief that they have it and by displaying the correct attitude. Tom is also dressed as the previously mentioned Thomas Shelby from Peaky Blinders, which is itself a double signifier. The name match (“Thomas”, get it?) and the suit-and-flat-cap costume turn the cartoon cat into a stand-in for the perhaps most used "high-aura" male character of the past decade, the brooding gangster patriarch whose every cigarette drag has been set to phonk, cinematic scores and electronic music a thousand times over. On top of that, he is made entirely out of chrome, a popular trope of asking ChatGPT (one of the few AI tools people have easy and broad access to) to render things out of very high quality materials to indicate "rarity" or "status" like diamonds, platinum and etc. A sign that itself descends from a longer lineage of in-game cosmetic rarity tiers (League of Legends, MMOs, various skin economy freemium game, the Fortnite battle pass, the Pokémon shiny, dacha games and etc) where material finish is the visual shorthand of value. So "chrome" or "platinum" Tom on top of all previous signifiers signals a “maximized” or “maxxd” version. The image is suppose to invoke the superlative highest possible tier, rarest-drop, legendary-rarity version of aura, the way a kid in a playground would describe their dad as not just strong but the strongest in the world. FOUR: Finally, the background black hole calls back to the original Tom image, where he is surrounded by the universe itself, having ascended. The character has transcended the diegetic frame of his own cartoon and now exists at a cosmological scale, with the black hole standing in for the kind of unmotivated, vibes-based "cosmic" imagery that has become the default background for any video trying to signify that something Big is happening (the same visual motif that has powered comic book characters, anime transformations, video game power ups and anything wants to feel grandiose or “epic” without specifying what about). The black hole means significance in the abstract. At this point I think you understand the mechanism at play here. None of these references resolve to a stable meaning on their own. Tom Tucker is “cool” only in the very short context in which his image served as a substrate; he was convenient footage to pair with a song, and the absurdity of doing an "aura edit" on such a minor, strange character scene makes it all funnier and easier to share. Tom-the-cat is doing the aura pose > the aura pose comes from the iShowSpeed girl > the iShowSpeed girl was cool because she correctly played her part in an established bit of a large streamer with the correct timing and theatrical flair > the bit was cool because it was a shared convention unified by a popular central streamer figure > the convention existed because phonk edits had already trained this exact scenario to be read as confidence-plus-detachment as aura > the chrome finish points to AI image generation quirks > the AI image generation style can be mapped to gaming visual rarity shorthands; the gaming rarity tiers point to a much older logic of precious-metal-as-status. Each step on the referential chain is propped by the one behind it, and the one behind it is propped up by the one behind that, so on and so forth. There is no natural endpoint, the entire structure functions more akin to a network than a linked list. If you stop at any single point and ask "but why is particular signifier cool or funny or interesting”, the answer is always "because of the thing behind it.” It’s hyper-citation, Here, what matters is the structure of the whole rather than the content. This is structure is what I mean by infinite referential mirrors. The rate at which a concept is referencing, remixing and calling back to another is what’s interesting. In other words, It’s the velocity that matters. The chain of recognitions, each "I get that reference," and the cumulative effect of getting six references stacked on top of each other a short span of time gives you the feeling that you are participating in something dense and alive, because it allows you to recognize the shared meme ecosystem of the platform that you are participating in, even if only a glimpse of it. You are inside the culture rather than outside it. The brainrot-curious reader who watches this video and feels nothing, has “failed” to understand the joke because they are outside the hall of mirrors I am describing. You can only get the magic if you step in and start counting the reflections: the song, the suit, the cat, the chrome, the black hole, the transitions the video uses. You are looking at connected parts of this network of symbols and at the speed at which one image hands you off to the next. The entire thirteen-second clip is functioning as a single compressed referential payload that decompresses in the viewer's head into a small private essay exactly like this one. The video allows you to recognize yourself as someone capable of decoding it, and that recognition is the reward. That’s why media like this goes viral.

Pleometric

69,255 views • 2 months ago