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🎥 Introducing Hailuo's Subject Reference: Revolutionizing Character Consistency in Video Creation 🔥 We’re excited to present Hailuo's S2V-01 model, a groundbreaking innovation in AI video generation that tackles one of the industry’s biggest challenges: maintaining consistent, realistic facial features and identity across dynamic video content, regardless of camera angles...

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Hailuo AI-MiniMax Hub

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692,515 görüntüleme • 1 yıl önce •via X (Twitter)

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reference images

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🚀 Introducing Freepik AI video generator. Everything you need to create high-quality, physically accurate videos in one place. 🤩

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The possibilities are endless… amazing🍎🫶

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Also accept me into your artist program 😭🫡

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Bullish on Hailuo and $apple

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Love it

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Just keeps getting better! 🍎

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Love it so much

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I think we crashed the site...

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the only limit is your own imagination! 🍎🐶

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It's ON! ;)

Benzer Videolar

🚀 Create stunning, character-consistent videos with Hailuo's Subject Reference (S2V-01) Model 🚀: ✨ Single-Image Simplicity: Unlock precise facial details from just one reference image. ⚡ Ultra-Low Cost: Achieve results at less than 1% of traditional computational demands. 🌟 High Flexibility: Enjoy creative freedom with simple text prompts. ⏳ Faster Results: Experience drastically reduced wait times and reliable outputs. Transform your video creation process today! 🎥
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🚀 Create stunning, character-consistent videos with Hailuo's Subject Reference (S2V-01) Model 🚀: ✨ Single-Image Simplicity: Unlock precise facial details from just one reference image. ⚡ Ultra-Low Cost: Achieve results at less than 1% of traditional computational demands. 🌟 High Flexibility: Enjoy creative freedom with simple text prompts. ⏳ Faster Results: Experience drastically reduced wait times and reliable outputs. Transform your video creation process today! 🎥

Hailuo AI-MiniMax Hub

631,662 görüntüleme • 1 yıl önce

GPT Image 2 - Character Reference Sheet Prompt You can create a character reference sheet in any style you want for use in your Seedance 2.0 videos. All you need to do is either provide a reference image or describe your character. You can also input random values to generate completely random characters. This reference sheet provides a clear, structured, and highly readable visual breakdown of the character, helping the model maintain strong identity consistency across different outputs. The layout ensures the model focuses on essential visual information rather than stylistic noise, while the inclusion of expressions, head angles, and performance cues improves the model’s ability to generate believable acting and varied poses without losing character integrity. All clips in the demonstration video were generated using the reference sheet image and the same Seedance 2.0 prompt. You can find the prompts in the replies. 👇 GPT Image 2 and Seedance 2.0 are both available on mitte.ai
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GPT Image 2 - Character Reference Sheet Prompt You can create a character reference sheet in any style you want for use in your Seedance 2.0 videos. All you need to do is either provide a reference image or describe your character. You can also input random values to generate completely random characters. This reference sheet provides a clear, structured, and highly readable visual breakdown of the character, helping the model maintain strong identity consistency across different outputs. The layout ensures the model focuses on essential visual information rather than stylistic noise, while the inclusion of expressions, head angles, and performance cues improves the model’s ability to generate believable acting and varied poses without losing character integrity. All clips in the demonstration video were generated using the reference sheet image and the same Seedance 2.0 prompt. You can find the prompts in the replies. 👇 GPT Image 2 and Seedance 2.0 are both available on mitte.ai

Kōda

160,571 görüntüleme • 3 ay önce

🚀 Introducing HunyuanCustom: An open-source, multimodal-driven architecture for customized video generation, powered by HunyuanVideo-13B. Outperforming existing open-source models, it rivals top closed-source solutions! 🎥 Highlights: ✅Subject Consistency: Maintains identity across single & multi-subject video generation. ✅Multimodal Inputs: Supports text, images, audio, and video for highly controlled outputs. ✅High-Quality Output: Industry-leading detail, motion smoothness, and lighting realism. Key Features: 1️⃣Single-Subject Video: Upload an image + text (e.g., “He’s walking a dog”) to create coherent videos with new actions, outfits, and scenes. 2️⃣Multi-Subject Video: Generate videos with multiple subjects (e.g., a man drinking coffee in a cozy room) from separate image inputs. 3️⃣Audio-Driven Video: Sync audio with visuals for talking or singing in any scene—perfect for digital avatars, virtual customer service, and more. 4️⃣Video-Driven Video: Seamlessly insert or replace subjects into any video for creative enhancements. Dive Deeper: 🌐Project: 📝Technical Details: 💻Code: The single-subject video capability is open-sourced and live on the Hunyuan website, with more features to be released soon! 👉 Try it now: #HunyuanCustom #VideoGeneration #AI #TencentHunyuan
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🚀 Introducing HunyuanCustom: An open-source, multimodal-driven architecture for customized video generation, powered by HunyuanVideo-13B. Outperforming existing open-source models, it rivals top closed-source solutions! 🎥 Highlights: ✅Subject Consistency: Maintains identity across single & multi-subject video generation. ✅Multimodal Inputs: Supports text, images, audio, and video for highly controlled outputs. ✅High-Quality Output: Industry-leading detail, motion smoothness, and lighting realism. Key Features: 1️⃣Single-Subject Video: Upload an image + text (e.g., “He’s walking a dog”) to create coherent videos with new actions, outfits, and scenes. 2️⃣Multi-Subject Video: Generate videos with multiple subjects (e.g., a man drinking coffee in a cozy room) from separate image inputs. 3️⃣Audio-Driven Video: Sync audio with visuals for talking or singing in any scene—perfect for digital avatars, virtual customer service, and more. 4️⃣Video-Driven Video: Seamlessly insert or replace subjects into any video for creative enhancements. Dive Deeper: 🌐Project: 📝Technical Details: 💻Code: The single-subject video capability is open-sourced and live on the Hunyuan website, with more features to be released soon! 👉 Try it now: #HunyuanCustom #VideoGeneration #AI #TencentHunyuan

Tencent HY

191,908 görüntüleme • 1 yıl önce

We’re bringing identity consistency to Veo 3.1. For the first time ever, you have complete control of your Veo generations across every voice, character, and scene. With HeyGen’s avatar technology, generate once in Veo 3.1 and your avatar stays consistent, keeping your real voice and true look every time. Voice Consistency: Choose your voice, and HeyGen blends your real tone into Veo’s cinematic motion. Multi-Scene Consistency: Seamless transitions between scenes, one identity, endless stories. Character Consistency: Keep using that avatar with consistent voice and appearance to create unlimited videos in different outfits, styles and environments This is what happens when the world’s best video generator meets the best avatar technology. Don’t just make a scene. Free your story.
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We’re bringing identity consistency to Veo 3.1. For the first time ever, you have complete control of your Veo generations across every voice, character, and scene. With HeyGen’s avatar technology, generate once in Veo 3.1 and your avatar stays consistent, keeping your real voice and true look every time. Voice Consistency: Choose your voice, and HeyGen blends your real tone into Veo’s cinematic motion. Multi-Scene Consistency: Seamless transitions between scenes, one identity, endless stories. Character Consistency: Keep using that avatar with consistent voice and appearance to create unlimited videos in different outfits, styles and environments This is what happens when the world’s best video generator meets the best avatar technology. Don’t just make a scene. Free your story.

HeyGen

1,864,191 görüntüleme • 9 ay önce

MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model with Gradio demo local demo: This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results, these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work, we introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity. To achieve this, we first develop a video diffusion model to encode temporal information. Second, to maintain the appearance coherence across frames, we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations, we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably, our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available.
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MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model with Gradio demo local demo: This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results, these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work, we introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity. To achieve this, we first develop a video diffusion model to encode temporal information. Second, to maintain the appearance coherence across frames, we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations, we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably, our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available.

AK

810,578 görüntüleme • 2 yıl önce

V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency
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V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency

AK

31,997 görüntüleme • 2 yıl önce

🚀 Introducing the Kling 3.0 Model: Everyone a Director. It’s Time. An all-in-one creative engine that enables truly native multimodal creation. - Superb Consistency: Your characters and elements, always locked in. - Flexible Video Production: Create 15s clips with precise control, excellent video realism and customizable multi-shots. - Upgraded Native Audio: Now supports multi-character reference, more languages and accents. - Enhanced Image Generation: 4K Image output, new image series mode and more cinematic visuals. Ultra subscribers get exclusive early access — now live on Web -> Kling AI. Get ready to unlock your creative potential with our most advanced model yet. [For the next 24 hours ONLY] Follow, Comment & Retweet to win exclusive early access - we will select 10 winners in this post from the top ten 'Most liked replies'. So which feature are you most excited about? Let us know in the comment! We will contact you via your DM.
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🚀 Introducing the Kling 3.0 Model: Everyone a Director. It’s Time. An all-in-one creative engine that enables truly native multimodal creation. - Superb Consistency: Your characters and elements, always locked in. - Flexible Video Production: Create 15s clips with precise control, excellent video realism and customizable multi-shots. - Upgraded Native Audio: Now supports multi-character reference, more languages and accents. - Enhanced Image Generation: 4K Image output, new image series mode and more cinematic visuals. Ultra subscribers get exclusive early access — now live on Web -> Kling AI. Get ready to unlock your creative potential with our most advanced model yet. [For the next 24 hours ONLY] Follow, Comment & Retweet to win exclusive early access - we will select 10 winners in this post from the top ten 'Most liked replies'. So which feature are you most excited about? Let us know in the comment! We will contact you via your DM.

Kling AI

1,195,432 görüntüleme • 5 ay önce

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.
0:30

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

AI at Meta

2,265,118 görüntüleme • 1 yıl önce

🎬 Cinematic Consistency in the Age of AI Video: Why Prompt Structure Matters ⠀ I created a custom GPT built specifically to solve one of the biggest problems in AI filmmaking: character and visual continuity across shots and sequences. ⠀ Too often, tools like Midjourney Video or Veo3 generate beautiful moments — but fall apart when you try to tell a story. Characters change. Outfits glitch. Locations shift. ⠀ This GPT helps with that. It’s trained to write highly structured cinematic prompts, with attention to: — Composition & camera motion — Character appearance and consistency — Lens & lighting consistency — Emotional tone, color grading, and even ambient audio cues ⠀ 💡 The result? A prompt system that actually thinks like a director and helps you like your DP or 1st AD. ⠀ Here’s a side-by-side comparison using the same simple sequence idea in Veo3 and Midjourney Video, both using my GPT. (don't judge the video result but the GPT assistance as I made this as quickly as possible) 👇
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🎬 Cinematic Consistency in the Age of AI Video: Why Prompt Structure Matters ⠀ I created a custom GPT built specifically to solve one of the biggest problems in AI filmmaking: character and visual continuity across shots and sequences. ⠀ Too often, tools like Midjourney Video or Veo3 generate beautiful moments — but fall apart when you try to tell a story. Characters change. Outfits glitch. Locations shift. ⠀ This GPT helps with that. It’s trained to write highly structured cinematic prompts, with attention to: — Composition & camera motion — Character appearance and consistency — Lens & lighting consistency — Emotional tone, color grading, and even ambient audio cues ⠀ 💡 The result? A prompt system that actually thinks like a director and helps you like your DP or 1st AD. ⠀ Here’s a side-by-side comparison using the same simple sequence idea in Veo3 and Midjourney Video, both using my GPT. (don't judge the video result but the GPT assistance as I made this as quickly as possible) 👇

Jesus Plaza

51,432 görüntüleme • 1 yıl önce

“Paying homage to Adobe’s classic ad—the one where creativity lights up the world. I recreated that vibe in just 5 hours with Kling O1. Adobe’s inspiration still shines, but Kling O1 makes creation jump from slow to effortless. Character consistency is rock-solid: based on a reference video, expressions, poses, and style stay aligned across shots. Editing is straightforward—import, select, remove, swap, or tweak with real-time previews. Perfect for style replacements, green-screen work, multi-angle generation, and turning a single image into a dynamic shot sequence. Bottom line: Kling O1 is fast, reliable, and creator-ready. Give it a spin. 🚀” — Kling AI Creative Partner @ BOB
1:00

“Paying homage to Adobe’s classic ad—the one where creativity lights up the world. I recreated that vibe in just 5 hours with Kling O1. Adobe’s inspiration still shines, but Kling O1 makes creation jump from slow to effortless. Character consistency is rock-solid: based on a reference video, expressions, poses, and style stay aligned across shots. Editing is straightforward—import, select, remove, swap, or tweak with real-time previews. Perfect for style replacements, green-screen work, multi-angle generation, and turning a single image into a dynamic shot sequence. Bottom line: Kling O1 is fast, reliable, and creator-ready. Give it a spin. 🚀” — Kling AI Creative Partner @ BOB

Kling AI

65,391 görüntüleme • 7 ay önce

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:
1:39

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,257 görüntüleme • 10 ay önce

Sora 2 consistent characters is absolutely insane 🤯 OpenAI just released the ability to create and save consistent characters in Sora 2. And this changes *everything* for AI UGC ads. Perfect for DTC brands & agencies who need the same creator across multiple videos in different scenes. Here's why this matters: Before this update, every Sora 2 video gave you a different person. You couldn't build brand consistency or create multi-video campaigns with the same "creator." Now you can: → Generate your ideal AI creator once (holding your product) → Save them as a consistent character in Sora 2 → Tag that character in unlimited video prompts → Same person, different scenes (car, bathroom, outdoors, etc.) → Build entire UGC campaigns with one consistent face Say "bye-bye" to spending $500 on a single UGC video. What you can create: → Multi-video testimonial series with the same person → Before/after campaigns with consistent character → Product demo sequences that feel cohesive → Entire ad creative libraries with your "brand ambassador" I recorded a 12-minute Loom showing the exact step-by-step process for creating consistent characters in Sora 2. Want the complete walkthrough? > Comment "SORA" > Like this post And I'll send the Loom over (must be following so I can DM)
0:21

Sora 2 consistent characters is absolutely insane 🤯 OpenAI just released the ability to create and save consistent characters in Sora 2. And this changes *everything* for AI UGC ads. Perfect for DTC brands & agencies who need the same creator across multiple videos in different scenes. Here's why this matters: Before this update, every Sora 2 video gave you a different person. You couldn't build brand consistency or create multi-video campaigns with the same "creator." Now you can: → Generate your ideal AI creator once (holding your product) → Save them as a consistent character in Sora 2 → Tag that character in unlimited video prompts → Same person, different scenes (car, bathroom, outdoors, etc.) → Build entire UGC campaigns with one consistent face Say "bye-bye" to spending $500 on a single UGC video. What you can create: → Multi-video testimonial series with the same person → Before/after campaigns with consistent character → Product demo sequences that feel cohesive → Entire ad creative libraries with your "brand ambassador" I recorded a 12-minute Loom showing the exact step-by-step process for creating consistent characters in Sora 2. Want the complete walkthrough? > Comment "SORA" > Like this post And I'll send the Loom over (must be following so I can DM)

Mike Futia

21,799 görüntüleme • 8 ay önce

Announcing Gemini 2.5 Flash Image! (Nano Banana) from Google DeepMind. This model brings you state of the art image generation! Here's 7m of demos. Today marks a leap forward in AI-powered image creativity with the launch of stronger native image generation and editing in Gemini 2.5 Flash. Here's what's new: 👤 Consistent character design: Maintain a character's identity across multiple images, changing their outfits, poses, and scenes without losing their core look. 🎨 Creative composition: Seamlessly merge elements from up to three different images into a single, cohesive masterpiece. The potential for surrealist art and unique compositions is endless. ✍️ Targeted edits with natural language: Go beyond the prompt. You can now make highly specific edits like "change the background," "restore this faded photo," or "edit the character's outfit" with simple commands. ✨ Design & identity adaptation: Apply a specific style, texture, or pattern from one image to another, unlocking new possibilities for fashion, product design, and architectural visualization. I've put together a ~7-minute video demo to walk you through these features and show just how powerful they are in action. We can't wait to see what you all create! Try the model out in Google AI Studio, Gemini app or the Gemini API.
7:27

Announcing Gemini 2.5 Flash Image! (Nano Banana) from Google DeepMind. This model brings you state of the art image generation! Here's 7m of demos. Today marks a leap forward in AI-powered image creativity with the launch of stronger native image generation and editing in Gemini 2.5 Flash. Here's what's new: 👤 Consistent character design: Maintain a character's identity across multiple images, changing their outfits, poses, and scenes without losing their core look. 🎨 Creative composition: Seamlessly merge elements from up to three different images into a single, cohesive masterpiece. The potential for surrealist art and unique compositions is endless. ✍️ Targeted edits with natural language: Go beyond the prompt. You can now make highly specific edits like "change the background," "restore this faded photo," or "edit the character's outfit" with simple commands. ✨ Design & identity adaptation: Apply a specific style, texture, or pattern from one image to another, unlocking new possibilities for fashion, product design, and architectural visualization. I've put together a ~7-minute video demo to walk you through these features and show just how powerful they are in action. We can't wait to see what you all create! Try the model out in Google AI Studio, Gemini app or the Gemini API.

Addy Osmani

159,059 görüntüleme • 11 ay önce

Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.
1:43

Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.

AK

40,474 görüntüleme • 2 yıl önce

Tencent presents GameGen-O Open-world Video Game Generation We introduce GameGen-O, the first diffusion transformer model tailored for the generation of open-world video games. This model facilitates high-quality, open-domain generation by simulating a wide array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, thus allowing for the gameplay simulation. The development of GameGen-O involves a comprehensive data collection and processing effort from scratch. We collect and build the first Open-World Video Game Dataset (OGameData), amassed extensive data from over a hundred of next-generation open-world games, employing a proprietary data pipeline for efficient sorting, scoring, filtering, and decoupled captioning. This robust and extensive OGameData forms the foundation of our model's training process. GameGen-O undergoes a two-stage training process, consisting of foundation model pretraining and instruction tuning. In the first phase, the model is pre-trained on the OGameData via the text-to-video and video continuation, endowing GameGen-O with the capability for open-domain video game generation. In the second phase, the pre-trained model is frozen, and we fine-tuned using a trainable InstructNet, which enables the production of subsequent frames based on multimodal structural instructions. This whole training process imparts the model with the ability to generate and interactively control content. In summary, GameGen-O represents a notable initial step forward in the realm of open-world video game generation via generative models. It underscores the potential of generative models to serve as an alternative to rendering techniques, which can efficiently combine creative generation with interactive capabilities.
0:51

Tencent presents GameGen-O Open-world Video Game Generation We introduce GameGen-O, the first diffusion transformer model tailored for the generation of open-world video games. This model facilitates high-quality, open-domain generation by simulating a wide array of game engine features, such as innovative characters, dynamic environments, complex actions, and diverse events. Additionally, it provides interactive controllability, thus allowing for the gameplay simulation. The development of GameGen-O involves a comprehensive data collection and processing effort from scratch. We collect and build the first Open-World Video Game Dataset (OGameData), amassed extensive data from over a hundred of next-generation open-world games, employing a proprietary data pipeline for efficient sorting, scoring, filtering, and decoupled captioning. This robust and extensive OGameData forms the foundation of our model's training process. GameGen-O undergoes a two-stage training process, consisting of foundation model pretraining and instruction tuning. In the first phase, the model is pre-trained on the OGameData via the text-to-video and video continuation, endowing GameGen-O with the capability for open-domain video game generation. In the second phase, the pre-trained model is frozen, and we fine-tuned using a trainable InstructNet, which enables the production of subsequent frames based on multimodal structural instructions. This whole training process imparts the model with the ability to generate and interactively control content. In summary, GameGen-O represents a notable initial step forward in the realm of open-world video game generation via generative models. It underscores the potential of generative models to serve as an alternative to rendering techniques, which can efficiently combine creative generation with interactive capabilities.

AK

367,088 görüntüleme • 1 yıl önce

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution paper page: Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency, which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video, a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally, it integrates temporal layers into U-Net and VAE-Decoder, maintaining consistency within short sequences; globally, without training, a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm, our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation, enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks, as well as in AI-generated videos, showcasing impressive visual realism and temporal consistency.
1:45

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution paper page: Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency, which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video, a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally, it integrates temporal layers into U-Net and VAE-Decoder, maintaining consistency within short sequences; globally, without training, a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm, our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation, enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks, as well as in AI-generated videos, showcasing impressive visual realism and temporal consistency.

AK

32,849 görüntüleme • 2 yıl önce

Today, we’re thrilled to announce the launch of Akool Live Camera — a breakthrough in real-time AI video generation and the newest innovation in our Akool Live Suite. This is more than a product launch — it's a new chapter in how we communicate, connect, and show up in the world. Akool Live Camera creates photorealistic, dynamic avatars that reflect your expressions, voice, gestures, and emotional tone — all in real time, in any language. Built for live interactions—not pre-recorded content—Akool Live Camera powers: 🌍 Live AI Video Translation – Speak in one language, appear fluent in another — lip-sync, voice, expressions & gestures included. 🎭 Live Face Swap – Keep your host’s identity consistent, even when they’re not live. 🧑‍💼 Real-Time AI Avatars – Branded, expressive avatars for privacy, support, and seamless comms. 💡 Real-Time Video Generation – Coming soon: speak your idea, and your video creates itself. In real time. This is the birth of a new category: Live AI Video Generation. This is live, unscripted, intelligent visual presence. 💡 Welcome to the future of video, where anyone, anywhere, can show up fully and authentically — in any language, at any time. 🔗 Get early access: 📣 Read the full announcement: #AkoolLiveCamera #AIvideo #LiveTranslation #AvatarTech #RealTimeVideo #RealTimeAI #GenerativeAI #SyntheticMedia #AIAvatars #LiveTranslation #FaceSwap #FutureOfVideo #AIInnovation #TechLaunch
4:49

Today, we’re thrilled to announce the launch of Akool Live Camera — a breakthrough in real-time AI video generation and the newest innovation in our Akool Live Suite. This is more than a product launch — it's a new chapter in how we communicate, connect, and show up in the world. Akool Live Camera creates photorealistic, dynamic avatars that reflect your expressions, voice, gestures, and emotional tone — all in real time, in any language. Built for live interactions—not pre-recorded content—Akool Live Camera powers: 🌍 Live AI Video Translation – Speak in one language, appear fluent in another — lip-sync, voice, expressions & gestures included. 🎭 Live Face Swap – Keep your host’s identity consistent, even when they’re not live. 🧑‍💼 Real-Time AI Avatars – Branded, expressive avatars for privacy, support, and seamless comms. 💡 Real-Time Video Generation – Coming soon: speak your idea, and your video creates itself. In real time. This is the birth of a new category: Live AI Video Generation. This is live, unscripted, intelligent visual presence. 💡 Welcome to the future of video, where anyone, anywhere, can show up fully and authentically — in any language, at any time. 🔗 Get early access: 📣 Read the full announcement: #AkoolLiveCamera #AIvideo #LiveTranslation #AvatarTech #RealTimeVideo #RealTimeAI #GenerativeAI #SyntheticMedia #AIAvatars #LiveTranslation #FaceSwap #FutureOfVideo #AIInnovation #TechLaunch

Akool Inc

6,175,751 görüntüleme • 1 yıl önce

As detailed in the Meta Movie Gen technical report, today we’re open sourcing Movie Gen Bench: two new media generation benchmarks that we hope will help to enable the AI research community to progress work on more capable audio and video generation models. Movie Gen Video Bench is the largest and most comprehensive benchmark ever released for evaluating text-to-video generation. It includes a collection of 1,000+ prompts that cover concepts ranging from detailed human activity to animals, physics, unusual subjects and more — with broad coverage across different motion levels. Movie Gen Audio Bench is a first-of-its-kind benchmark aimed at evaluating video-to-audio and (text+video)-to-audio generation. It includes 527 generated videos and associated sound effects and music prompts covering a diverse set of ambient environments and sound effects. To enable fair and easy comparison to our models for future works, these new benchmarks include non cherry-picked generated videos and audio from Movie Gen. In releasing these new benchmarks we hope to promote fair & extensive evaluations in media generation research to enable greater progress in this field.
0:23

As detailed in the Meta Movie Gen technical report, today we’re open sourcing Movie Gen Bench: two new media generation benchmarks that we hope will help to enable the AI research community to progress work on more capable audio and video generation models. Movie Gen Video Bench is the largest and most comprehensive benchmark ever released for evaluating text-to-video generation. It includes a collection of 1,000+ prompts that cover concepts ranging from detailed human activity to animals, physics, unusual subjects and more — with broad coverage across different motion levels. Movie Gen Audio Bench is a first-of-its-kind benchmark aimed at evaluating video-to-audio and (text+video)-to-audio generation. It includes 527 generated videos and associated sound effects and music prompts covering a diverse set of ambient environments and sound effects. To enable fair and easy comparison to our models for future works, these new benchmarks include non cherry-picked generated videos and audio from Movie Gen. In releasing these new benchmarks we hope to promote fair & extensive evaluations in media generation research to enable greater progress in this field.

AI at Meta

156,273 görüntüleme • 1 yıl önce

XAI EXTENDS GROK IMAGINE VIDEO GENERATION TO 10 SECONDS WITH QUALITY ENHANCEMENTS xAI has updated its Grok Imagine tool to produce videos lasting 10 seconds, doubling the prior limit. This change, along with refinements in visual and audio elements, expands the tool's utility for short-form content creation. xAI released the upgrade to Grok Imagine in early 2026. The company, founded by Elon Musk, announced the feature through posts on the X platform. This follows previous iterations where videos were capped at shorter durations, typically 5 seconds. Grok Imagine allows users to generate videos from text prompts, building on its image creation capabilities. The update addresses constraints in video length that limited expressive potential. Users can now input descriptions to create clips, such as animations or scenes, without needing initial images. This positions the tool within the broader landscape of AI-driven multimodal generation, where text-to-video systems are increasingly common. The core adjustment doubles the maximum video duration from 5 seconds to 10 seconds. Accompanying this are upgrades to video quality, including more stable visuals, richer details, and improved clarity. Audio has also been enhanced for better output, making the generated content more immersive. These changes were described as "big improvements across the board" in the announcement. No specific benchmarks or quantitative metrics for the quality improvements were detailed in the release statements. The feature rollout appears gradual, with some users accessing it via the Grok app or web interface. xAI has not introduced user controls for exact timing, though such options are mentioned as future possibilities. This development highlights xAI's emphasis on iterative enhancements in generative AI tools. By extending duration while refining output fidelity, it reflects engineering priorities aimed at balancing computational efficiency with user needs. The focus on audio and visual stability suggests attention to common pitfalls in early text-to-video models, such as inconsistencies or artifacts. The sources do not specify the underlying model architecture changes or training data adjustments enabling this upgrade. Performance in real-world scenarios, like handling complex prompts or maintaining consistency across clips, remains unquantified in the announcements. Interpretations of broader implications for AI video generation would require additional evidence beyond what's provided.
0:29

XAI EXTENDS GROK IMAGINE VIDEO GENERATION TO 10 SECONDS WITH QUALITY ENHANCEMENTS xAI has updated its Grok Imagine tool to produce videos lasting 10 seconds, doubling the prior limit. This change, along with refinements in visual and audio elements, expands the tool's utility for short-form content creation. xAI released the upgrade to Grok Imagine in early 2026. The company, founded by Elon Musk, announced the feature through posts on the X platform. This follows previous iterations where videos were capped at shorter durations, typically 5 seconds. Grok Imagine allows users to generate videos from text prompts, building on its image creation capabilities. The update addresses constraints in video length that limited expressive potential. Users can now input descriptions to create clips, such as animations or scenes, without needing initial images. This positions the tool within the broader landscape of AI-driven multimodal generation, where text-to-video systems are increasingly common. The core adjustment doubles the maximum video duration from 5 seconds to 10 seconds. Accompanying this are upgrades to video quality, including more stable visuals, richer details, and improved clarity. Audio has also been enhanced for better output, making the generated content more immersive. These changes were described as "big improvements across the board" in the announcement. No specific benchmarks or quantitative metrics for the quality improvements were detailed in the release statements. The feature rollout appears gradual, with some users accessing it via the Grok app or web interface. xAI has not introduced user controls for exact timing, though such options are mentioned as future possibilities. This development highlights xAI's emphasis on iterative enhancements in generative AI tools. By extending duration while refining output fidelity, it reflects engineering priorities aimed at balancing computational efficiency with user needs. The focus on audio and visual stability suggests attention to common pitfalls in early text-to-video models, such as inconsistencies or artifacts. The sources do not specify the underlying model architecture changes or training data adjustments enabling this upgrade. Performance in real-world scenarios, like handling complex prompts or maintaining consistency across clips, remains unquantified in the announcements. Interpretations of broader implications for AI video generation would require additional evidence beyond what's provided.

Lacey

28,881 görüntüleme • 6 ay önce

Elon Musk: Despite starting from scratch six months ago, Grok Imagine now generates more videos and images than everyone else combined. “My prediction is that most of AI compute is going to be real-time video understanding and real-time video generation. And we expect to be the leaders in that. It's worth emphasizing these points that six months ago we had basically nothing, very weak in video and image generation and editing and went in six months to the number one spot and in fact generating more videos and images than everyone else combined. We're going to do the same thing with coding and we're going to do the same thing with Macrohard and. I think people will be pretty impressed with the Grok 4.2 model that's coming out. It's a significant improvement. That's the small version of our new model. So we'll have a medium and a large version that are even more intelligent.” From: xAI All-Hands, February 10, 2026
0:42

Elon Musk: Despite starting from scratch six months ago, Grok Imagine now generates more videos and images than everyone else combined. “My prediction is that most of AI compute is going to be real-time video understanding and real-time video generation. And we expect to be the leaders in that. It's worth emphasizing these points that six months ago we had basically nothing, very weak in video and image generation and editing and went in six months to the number one spot and in fact generating more videos and images than everyone else combined. We're going to do the same thing with coding and we're going to do the same thing with Macrohard and. I think people will be pretty impressed with the Grok 4.2 model that's coming out. It's a significant improvement. That's the small version of our new model. So we'll have a medium and a large version that are even more intelligent.” From: xAI All-Hands, February 10, 2026

ELON CLIPS

12,007 görüntüleme • 5 ay önce