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Orihime ✨ Image generated by Balecxi Video generated with Kling 2.1 75+ exclusives on p*treon (link in bio) #Orihimeinoue #BLEACH #r34 #animeart #waifu #aiart #animation #animated

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stayblocky_ai

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12,728 görüntüleme • 1 yıl önce •via X (Twitter)

Sanat#Orihimeinoue#BLEACH#r34#animeart#waifu#aiart#animation#animated
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Generated this video by chatting with a single agent ✨ I told GLIF that I wanted to create this anime short. It made the storyboard with GPT-Image-2 and then animated it with Seedance 2. Such an easy way to get controllable video generation!
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Generated this video by chatting with a single agent ✨ I told GLIF that I wanted to create this anime short. It made the storyboard with GPT-Image-2 and then animated it with Seedance 2. Such an easy way to get controllable video generation!

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NEW tutorial! How to draw consistent proportions on animation - Tutorial of my personal technique - Real time video process of the dancing head animation - Frames of the dancing head for free use to download, study, animate your characters Available on my P-treon, link in bio!!

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Wow. AI-generated animation from a video. This experiment combines video of dance choreography by Erica Klein and hok with AnimateDiff and a text prompt. #AnimateDiff feels like the missing link in diffusion video generation -no flickering! More soon. #stablediffusion

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What does all this mean for culture? If you are watching my AI Influencer list here on X you'll see a ton of people posting various videos generated by AI, like this one that Justine posted last week, done with Kling AI Tonight I had some fun. My wife shot a video of me crawling on the floor being a goofball. Kling turned me into an evil 1X robot in a few minutes. Ahh, the kids and I are gonna have some fun with this one! Done with kling 2.6 motion control.
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What does all this mean for culture? If you are watching my AI Influencer list here on X you'll see a ton of people posting various videos generated by AI, like this one that Justine posted last week, done with Kling AI Tonight I had some fun. My wife shot a video of me crawling on the floor being a goofball. Kling turned me into an evil 1X robot in a few minutes. Ahh, the kids and I are gonna have some fun with this one! Done with kling 2.6 motion control.

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There's a popular rumor that storyboards completely ruin character consistency in AI animation. Well, yes and no. 😅 For this video, I generated myself in a bright new look, created a sketch storyboard in GPT Image 2, and animated it with Seedance 2.0. From my experience, if your storyboard is in hyper-realism, the likeness definitely tends to drift. But if you keep it strictly as a sketch? The face stays surprisingly consistent. I’m sharing the exact prompts I used for both the storyboard and the animation in the thread below! 👇
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There's a popular rumor that storyboards completely ruin character consistency in AI animation. Well, yes and no. 😅 For this video, I generated myself in a bright new look, created a sketch storyboard in GPT Image 2, and animated it with Seedance 2.0. From my experience, if your storyboard is in hyper-realism, the likeness definitely tends to drift. But if you keep it strictly as a sketch? The face stays surprisingly consistent. I’m sharing the exact prompts I used for both the storyboard and the animation in the thread below! 👇

Ivanna | AI Art & Prompts

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Watching a character come to life from literally nothing hits different From a blank white canvas to a fully rendered character — no shortcuts, no magic snap, just pure sketch-to-color-to-polish energy. Every stroke, every layer, every color pick you see actually built this look step by step The best part? This entire animation was generated in just a few clicks: upload a reference image, drop in a prompt, pick your model, and let AI handle the rest 🤯 That's the power of AI video generation now. Made with MiniMax H3 on Fish Creative The prompt is in the comments 👇
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Watching a character come to life from literally nothing hits different From a blank white canvas to a fully rendered character — no shortcuts, no magic snap, just pure sketch-to-color-to-polish energy. Every stroke, every layer, every color pick you see actually built this look step by step The best part? This entire animation was generated in just a few clicks: upload a reference image, drop in a prompt, pick your model, and let AI handle the rest 🤯 That's the power of AI video generation now. Made with MiniMax H3 on Fish Creative The prompt is in the comments 👇

Shami

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What if your video pipeline worked like your codebase? Introducing Replit Animation. Replit ⠕ #replitanimation From a single prompt, you can generate animated videos directly in Replit. It writes TypeScript with modern libraries to build scenes, apply styling, and export straight to mp4. Because it is built on code, you can import your real components, brand tokens, and design systems. That means consistent styling, reusable assets, and total control. Video as code means you can tweak, version, and scale your videos just like software. Create repeatable workflows for product updates, feature drops, and marketing campaigns without starting from scratch every time. Here is an example of a video I generated inside Replit. Export at your chosen resolution and frame rate or host with a unique URL. Activate video mode by typing video in your first prompt or selecting Animation under App mode. Create your first one:
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What if your video pipeline worked like your codebase? Introducing Replit Animation. Replit ⠕ #replitanimation From a single prompt, you can generate animated videos directly in Replit. It writes TypeScript with modern libraries to build scenes, apply styling, and export straight to mp4. Because it is built on code, you can import your real components, brand tokens, and design systems. That means consistent styling, reusable assets, and total control. Video as code means you can tweak, version, and scale your videos just like software. Create repeatable workflows for product updates, feature drops, and marketing campaigns without starting from scratch every time. Here is an example of a video I generated inside Replit. Export at your chosen resolution and frame rate or host with a unique URL. Activate video mode by typing video in your first prompt or selecting Animation under App mode. Create your first one:

JOY JULIET

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We're investing in Hedra, a company leading the way in AI character animation for video. AI video has come a long way, but when it comes to expressive, talking characters, most tools fall straight into the uncanny valley. Hedra is solving this by making it easy to generate character-driven scenes from any image and audio or script. Unlike other platforms, we believe its outputs are uniquely coherent, engaging, and natural. Nearly 3M users—from meme makers to enterprise marketers—have generated 10M+ videos on Hedra. If you’ve been on social media lately, you’ve probably seen one. We’ve been fortunate to know Michael Lingelbach and the team since their earliest days in our a16z speedrun 🧊 program, and have consistently been impressed by their velocity and blend of technical depth and product thinking. Link below to read more from Justine Moore, Troy Kirwin, Jon Lai, and Matt Bornstein.
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We're investing in Hedra, a company leading the way in AI character animation for video. AI video has come a long way, but when it comes to expressive, talking characters, most tools fall straight into the uncanny valley. Hedra is solving this by making it easy to generate character-driven scenes from any image and audio or script. Unlike other platforms, we believe its outputs are uniquely coherent, engaging, and natural. Nearly 3M users—from meme makers to enterprise marketers—have generated 10M+ videos on Hedra. If you’ve been on social media lately, you’ve probably seen one. We’ve been fortunate to know Michael Lingelbach and the team since their earliest days in our a16z speedrun 🧊 program, and have consistently been impressed by their velocity and blend of technical depth and product thinking. Link below to read more from Justine Moore, Troy Kirwin, Jon Lai, and Matt Bornstein.

a16z

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Contact sheet prompting is the hottest AI video technique right now 🤯 One image in → 6 consistent frames out → cinematic video ads in minutes. But everyone's doing it manually. I automated the entire workflow in n8n + Airtable. Here's why contact sheet prompting is blowing up: You give AI one reference image, and it generates a grid of consistent shots — same face, same outfit, different angles. Instant storyboarding, full creative control, no photoshoots. The problem? It's super tedious: → Write the prompt manually → Generate the contact sheet → Crop each frame by hand → Feed frames into a video model one by one → Repeat for every product This n8n automation handles all of it: → Upload character image + product image → AI analyzes both and writes the contact sheet prompt → Nano Banana Pro generates a 6-frame grid → System extracts each frame automatically → Kling 2.5 generates smooth transitions between frames → You get 5 video clips ready to stitch Approval checkpoints at every stage, no surprises. What lands in your Airtable: → AI-generated creative prompt → Core hero image (model + product) → 6-frame contact sheet → 5 cinematic video clips → Full control before each generation step Contact sheet prompting on autopilot. I filmed a 20 minute Loom video showing you exactly how I set it up. Want the Loom + the complete n8n workflow + Airtable base? > Comment "SHEET" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

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Iris Out - レゼダンス - Iris Out - Reze dance - Iris Out - 蕾潔舞 —————————————— Created by KlingAI Kling AI ✨ Try Kling Motion Control right now! It’s super easy to use, but the results are anything but simple. How to use: 1.Pick a character image you want to animate 2.Choose a reference video for the motion 3.Let Kling work its magic—one click and your character comes alive! Also, the video posted below was generated with Kling as well. —— 今すぐ Kling Motion Control を試してみよう！ 使い方は簡単なのに、仕上がりは想像以上。 使い方： 1.アニメーションを付けたい人物画像を選ぶ 2.参照したい動きの動画を選ぶ 3.Kling に任せて魔法をかけよう。ワンクリックでキャラが動き出す！ ちなみに、下に投稿した動画も Kling で生成したものです。 —— 現在就試試看 Kling Motion Control 吧！ 操作很簡單，效果卻一點都不簡單。 使用方式： 1.選一張你想套用動畫效果的人物圖 2.選一支你要參照動作的影片 3.交給 Kling 施展魔法，一鍵讓角色動起來！ 下面那篇發文的影片也是Kling生成的 #KlingAIDance
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Iris Out - レゼダンス - Iris Out - Reze dance - Iris Out - 蕾潔舞 —————————————— Created by KlingAI Kling AI ✨ Try Kling Motion Control right now! It’s super easy to use, but the results are anything but simple. How to use: 1.Pick a character image you want to animate 2.Choose a reference video for the motion 3.Let Kling work its magic—one click and your character comes alive! Also, the video posted below was generated with Kling as well. —— 今すぐ Kling Motion Control を試してみよう！ 使い方は簡単なのに、仕上がりは想像以上。 使い方： 1.アニメーションを付けたい人物画像を選ぶ 2.参照したい動きの動画を選ぶ 3.Kling に任せて魔法をかけよう。ワンクリックでキャラが動き出す！ ちなみに、下に投稿した動画も Kling で生成したものです。 —— 現在就試試看 Kling Motion Control 吧！ 操作很簡單，效果卻一點都不簡單。 使用方式： 1.選一張你想套用動畫效果的人物圖 2.選一支你要參照動作的影片 3.交給 Kling 施展魔法，一鍵讓角色動起來！ 下面那篇發文的影片也是Kling生成的 #KlingAIDance

BeautyVerse

77,375 görüntüleme • 8 ay önce

🔥 VIDU Multi-Entity Consistency Give Vidu 2/3 images and it’ll turn them into a video—it’s pure magic! ✨ Your own characters interacting with objects and in the exact environment you want! Ads, movies… endless possibilities, and this is just the beginning! Thanks @Viduforhuman The future is a carrot! 🥕 Plus, how about grabbing any frame from a Vidu-generated video "from scratch" and throwing it into another AI video or image tool to push your project even further? For now, check out the comment below: I scaled up a frame with Magnific.ai and fed it into Runway to create a dynamic shot using full camera control. But fingers crossed I can soon use #ReCapture by Bisho & team to generate new shots from the same video!
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🔥 VIDU Multi-Entity Consistency Give Vidu 2/3 images and it’ll turn them into a video—it’s pure magic! ✨ Your own characters interacting with objects and in the exact environment you want! Ads, movies… endless possibilities, and this is just the beginning! Thanks @Viduforhuman The future is a carrot! 🥕 Plus, how about grabbing any frame from a Vidu-generated video "from scratch" and throwing it into another AI video or image tool to push your project even further? For now, check out the comment below: I scaled up a frame with Magnific.ai and fed it into Runway to create a dynamic shot using full camera control. But fingers crossed I can soon use #ReCapture by Bisho & team to generate new shots from the same video!

Hungry Donkey 🥕

37,561 görüntüleme • 1 yıl önce

Contact sheet prompting is the hottest AI video technique right now 🤯 If you've seen this technique blowing up, here's why it works: You feed AI one image, and it generates a grid of consistent shots—same face, same outfit, different angles and poses. Instant storyboarding. Full creative control. No reshoots. But doing it manually is brutal: → Write the prompt from scratch → Generate the contact sheet → Crop each frame by hand → Feed frames into a video model one at a time → Repeat for every single product That's hours of work per campaign. This n8n automation handles everything: → Upload a character image + product image → AI analyzes both and writes the contact sheet prompt → Nano Banana Pro generates a 6-frame grid → System extracts each frame automatically → Kling 2.5 creates smooth transitions between frames → 5 video clips land in Airtable ready to use No manual cropping. No frame-by-frame prompting. No tedious busywork. What you get in Airtable: - AI-generated creative prompt - Hero image (model + product) - Full 6-frame contact sheet - 5 cinematic video clips - Approval gates before each step All inside n8n + Airtable. Contact sheet prompting on complete autopilot. I recorded a 20-minute Loom showing exactly how I built this. Want the walkthrough + the full n8n workflow + Airtable base? > Like this post > Comment "CONTACT" And I'll send it over (must be following so I can DM)
0:13

Contact sheet prompting is the hottest AI video technique right now 🤯 If you've seen this technique blowing up, here's why it works: You feed AI one image, and it generates a grid of consistent shots—same face, same outfit, different angles and poses. Instant storyboarding. Full creative control. No reshoots. But doing it manually is brutal: → Write the prompt from scratch → Generate the contact sheet → Crop each frame by hand → Feed frames into a video model one at a time → Repeat for every single product That's hours of work per campaign. This n8n automation handles everything: → Upload a character image + product image → AI analyzes both and writes the contact sheet prompt → Nano Banana Pro generates a 6-frame grid → System extracts each frame automatically → Kling 2.5 creates smooth transitions between frames → 5 video clips land in Airtable ready to use No manual cropping. No frame-by-frame prompting. No tedious busywork. What you get in Airtable: - AI-generated creative prompt - Hero image (model + product) - Full 6-frame contact sheet - 5 cinematic video clips - Approval gates before each step All inside n8n + Airtable. Contact sheet prompting on complete autopilot. I recorded a 20-minute Loom showing exactly how I built this. Want the walkthrough + the full n8n workflow + Airtable base? > Like this post > Comment "CONTACT" And I'll send it over (must be following so I can DM)

Mike Futia

24,609 görüntüleme • 8 ay önce

Wan 3.0 is live on Magnific. One generation now takes 20 references at once: 10 images, 5 audio, 5 videos. Not one style ref. Twenty. What that actually changes: Length : 2 to 30 seconds in a single pass. No stitching, no extending clip by clip and praying the character survives the cut. Native audio : voice, ambience and SFX generated with the video, not laid on top afterwards. Multiple ratios available , and adaptive the model composes for the frame instead of cropping into it. 10 image refs : a character, a product, a location and a lighting look can all hold in the same shot. ⚡ 40% off until September 30. More details in comments 👇 What's the first thing you'd throw 20 references at?
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Wan 3.0 is live on Magnific. One generation now takes 20 references at once: 10 images, 5 audio, 5 videos. Not one style ref. Twenty. What that actually changes: Length : 2 to 30 seconds in a single pass. No stitching, no extending clip by clip and praying the character survives the cut. Native audio : voice, ambience and SFX generated with the video, not laid on top afterwards. Multiple ratios available , and adaptive the model composes for the frame instead of cropping into it. 10 image refs : a character, a product, a location and a lighting look can all hold in the same shot. ⚡ 40% off until September 30. More details in comments 👇 What's the first thing you'd throw 20 references at?

LudovicCreator

24,012 görüntüleme • 24 gün önce

Mexican fashion brand Barragán has ignited backlash with the launch of a jacket directly inspired by the one Joaquín “El Chapo” Guzmán was wearing when he was first arrested in 1993. The piece, part of a new collection, features a deliberate red stain on the inner lining and was promoted with an AI-generated video depicting a youthful El Chapo in a setting that mimics the Altiplano maximum-security prison. Social media reactions have been sharply divided: many users condemn the brand for glorifying organized crime and profiting off the image of a man responsible for thousands of deaths, while others defend it as a provocative commentary on, or simply a savvy play into, Mexico’s narco-aesthetic. The jacket’s primary reference is the iconic 1993 arrest photograph of Guzmán Loera, an image that has long been mythologized in Mexican popular culture. As of now, Barragán has not released an official statement addressing the controversy.
0:11

Mexican fashion brand Barragán has ignited backlash with the launch of a jacket directly inspired by the one Joaquín “El Chapo” Guzmán was wearing when he was first arrested in 1993. The piece, part of a new collection, features a deliberate red stain on the inner lining and was promoted with an AI-generated video depicting a youthful El Chapo in a setting that mimics the Altiplano maximum-security prison. Social media reactions have been sharply divided: many users condemn the brand for glorifying organized crime and profiting off the image of a man responsible for thousands of deaths, while others defend it as a provocative commentary on, or simply a savvy play into, Mexico’s narco-aesthetic. The jacket’s primary reference is the iconic 1993 arrest photograph of Guzmán Loera, an image that has long been mythologized in Mexican popular culture. As of now, Barragán has not released an official statement addressing the controversy.

Cartel Watch

725,644 görüntüleme • 9 ay önce

✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan
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✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan

@levelsio

112,930 görüntüleme • 1 yıl önce

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

29,342 görüntüleme • 2 ay önce

Over the past few weeks, I’ve been looking for different ways to access Seedance 2.0. Then TopviewAI told me that their platform supports Seedance 2.0 — and it can also seamlessly combine multiple clips together. I used the prompt below to create this video, and the result is absolutely amazing! Use 🩵Image 1 as the first frame, referencing the character design, outfit color palette, and overall visual style of 🩵Image 1. The girl is performing a high-speed downhill skateboard ride on a winding suburban mountain road. The shot uses a Steadicam follow perspective, with an intense sense of speed throughout. The powerful wind generated by the fast ride makes her hair and clothing whip violently in the air. At the beginning, the girl pushes off with one foot to gain speed, then lowers her body to reduce wind resistance and continues accelerating. The scene features heavy motion blur to emphasize the extreme speed of the skateboard. While riding, she repeatedly shifts her center of gravity downward and leans left and right through multiple turns on the road. As she carves into the corners, the arm on the inside of the turn lowers as if lightly trying to touch the ground. On straight sections, she bends forward, keeps her knees low, and places both hands behind her back to minimize drag. In the distance, fireworks are going off above a seaside town, while a passenger airplane flies across the sky. The overall visual style should be ultra-realistic, with highly lifelike image quality and realistic photographic cinematography. No background music, only environmental sound design.
0:15

Over the past few weeks, I’ve been looking for different ways to access Seedance 2.0. Then TopviewAI told me that their platform supports Seedance 2.0 — and it can also seamlessly combine multiple clips together. I used the prompt below to create this video, and the result is absolutely amazing! Use 🩵Image 1 as the first frame, referencing the character design, outfit color palette, and overall visual style of 🩵Image 1. The girl is performing a high-speed downhill skateboard ride on a winding suburban mountain road. The shot uses a Steadicam follow perspective, with an intense sense of speed throughout. The powerful wind generated by the fast ride makes her hair and clothing whip violently in the air. At the beginning, the girl pushes off with one foot to gain speed, then lowers her body to reduce wind resistance and continues accelerating. The scene features heavy motion blur to emphasize the extreme speed of the skateboard. While riding, she repeatedly shifts her center of gravity downward and leans left and right through multiple turns on the road. As she carves into the corners, the arm on the inside of the turn lowers as if lightly trying to touch the ground. On straight sections, she bends forward, keeps her knees low, and places both hands behind her back to minimize drag. In the distance, fireworks are going off above a seaside town, while a passenger airplane flies across the sky. The overall visual style should be ultra-realistic, with highly lifelike image quality and realistic photographic cinematography. No background music, only environmental sound design.

underwood

12,541 görüntüleme • 5 ay önce

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

27,171 görüntüleme • 4 ay önce