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Seedance 2.0 has a fix for character drift almost nobody uses. Every new text description is a fresh interpretation of appearance. That's where the drift comes from: a slightly different face, a slightly different outfit, proportions that shift from scene to scene. The @ Image tag solves this directly...

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Zentrix⌚️

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88,931 görüntüleme • 28 gün önce •via X (Twitter)

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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.
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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⌚️

14,015 görüntüleme • 1 ay önce

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild
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You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

Rez Karim

10,951 görüntüleme • 2 ay önce

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)
0:18

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

11,126 görüntüleme • 2 ay önce

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.
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THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.

Nexlow

85,251 görüntüleme • 1 ay önce

Just watched Seedance 2.0 generate a full cinematic Video from a simple prompt text. No studio. No crew. No editing. Seedance 2.0 is now live on insMind and it's genuinely different — the camera movement, the lighting, the motion... it doesn't feel like "AI video." it feels like a director had a budget. If you're new: →every user gets 2 free Seedance 2.0 generations just for signing up. → 40% OFF credits only for a limited time. → 55% OFF yearly plan. Go try it → #insmind #insmindai
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Just watched Seedance 2.0 generate a full cinematic Video from a simple prompt text. No studio. No crew. No editing. Seedance 2.0 is now live on insMind and it's genuinely different — the camera movement, the lighting, the motion... it doesn't feel like "AI video." it feels like a director had a budget. If you're new: →every user gets 2 free Seedance 2.0 generations just for signing up. → 40% OFF credits only for a limited time. → 55% OFF yearly plan. Go try it → #insmind #insmindai

Leonardo

35,502 görüntüleme • 4 ay önce

🇨🇳 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 görüntüleme • 11 ay önce

Seedance 1.0 multi-shot is so powerful. It's one of the strongest AI video models I've found that can give you different angles from a single image. It's great for consistency and am hoping this becomes a normalized feature across more tools. Some examples.
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Seedance 1.0 multi-shot is so powerful. It's one of the strongest AI video models I've found that can give you different angles from a single image. It's great for consistency and am hoping this becomes a normalized feature across more tools. Some examples.

Jerrod Lew

23,778 görüntüleme • 10 ay önce

This demo was generated from a single image of a lipstick. I gave Wizstar AI a single lipstick photo, and it built the whole ugc ad: script, scene, model, product usage, and final video. no shoot required. here’s exactly how it works ↓
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This demo was generated from a single image of a lipstick. I gave Wizstar AI a single lipstick photo, and it built the whole ugc ad: script, scene, model, product usage, and final video. no shoot required. here’s exactly how it works ↓

Farhan

23,218 görüntüleme • 1 ay önce

gemini omniflash is actually f*cking cracked. you can animate/edit any video with a text prompt. character swaps, object transforms, full environment changes without regenerating/rotoscoping. everyone using AI to to animate and edit videos right now hits the same wall. the clip comes out 90% right and you regenerate from scratch hoping the 10% fixes itself. it never does. the fix is using your video as the input. omniflash edits what's already there instead of rolling the dice again. here's what's in the system: > the two-layer premiere trick: generate the same shot twice (one with background removed), stack them, cut at one frame, instant scene change > character swap with a single reference image (plus the one line you need or the model keeps the original's features) > object transforms that leave the rest of the frame untouched: stone into glowing sphere, candles into flowers > style transfer from an image reference instead of text, way more accurate > why stacking edits in one prompt breaks everything and the exact step order that doesn't > the audio limitation nobody mentions and how to work around it i packaged every prompt, the edit sequence, and the premiere layering setup. RT + reply "OMNI" and i'll send it over.
0:19

gemini omniflash is actually f*cking cracked. you can animate/edit any video with a text prompt. character swaps, object transforms, full environment changes without regenerating/rotoscoping. everyone using AI to to animate and edit videos right now hits the same wall. the clip comes out 90% right and you regenerate from scratch hoping the 10% fixes itself. it never does. the fix is using your video as the input. omniflash edits what's already there instead of rolling the dice again. here's what's in the system: > the two-layer premiere trick: generate the same shot twice (one with background removed), stack them, cut at one frame, instant scene change > character swap with a single reference image (plus the one line you need or the model keeps the original's features) > object transforms that leave the rest of the frame untouched: stone into glowing sphere, candles into flowers > style transfer from an image reference instead of text, way more accurate > why stacking edits in one prompt breaks everything and the exact step order that doesn't > the audio limitation nobody mentions and how to work around it i packaged every prompt, the edit sequence, and the premiere layering setup. RT + reply "OMNI" and i'll send it over.

Sulfur

36,390 görüntüleme • 1 ay önce

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.
0:18

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

108,957 görüntüleme • 29 gün önce

Testing SCAIL-2, an open source model for motion transfer. I wanted to see how it can handle driving videos with fast, dynamic movement. The part that really impressed me was how the model retained details from the input character's outfit (especially the straps on the shorts). I attached the input character below for you to compare against. The model also has a replacement mode that swaps the character into the driving video's scene, but here I used animation mode, which keeps the reference image's scene instead.
0:18

Testing SCAIL-2, an open source model for motion transfer. I wanted to see how it can handle driving videos with fast, dynamic movement. The part that really impressed me was how the model retained details from the input character's outfit (especially the straps on the shorts). I attached the input character below for you to compare against. The model also has a replacement mode that swaps the character into the driving video's scene, but here I used animation mode, which keeps the reference image's scene instead.

rob - comfyui

29,084 görüntüleme • 1 ay önce

Major major props to aroha for solving a major limitation with Seedance 2.0. If you've tried uploading audio files directly, you know that Seedance 2.0 actually changes the lyrics and even the song itself BUT if you save out a blank video with the song and upload that video as a video omni reference it gives you perfect lipsync and no style drifting of the audio. I personally haven't notice style drift but it's 100% better than a straight audio upload which makes it unusable. This is actually MAJOR!!! Those music videos you've been wanting to generate with Seedance 2.0 are now solved. I tested this myself with some audio from Suno 5.5 which is the example you're watching right now and it works! I just wish Seedance 2.0 would actually fix the audio upload problem but this is an immediate fix. You don't need to type out the lyrics like I did. I think the best use case for using just an audio file is only if you want to keep a characters voice consistent.
0:15

Major major props to aroha for solving a major limitation with Seedance 2.0. If you've tried uploading audio files directly, you know that Seedance 2.0 actually changes the lyrics and even the song itself BUT if you save out a blank video with the song and upload that video as a video omni reference it gives you perfect lipsync and no style drifting of the audio. I personally haven't notice style drift but it's 100% better than a straight audio upload which makes it unusable. This is actually MAJOR!!! Those music videos you've been wanting to generate with Seedance 2.0 are now solved. I tested this myself with some audio from Suno 5.5 which is the example you're watching right now and it works! I just wish Seedance 2.0 would actually fix the audio upload problem but this is an immediate fix. You don't need to type out the lyrics like I did. I think the best use case for using just an audio file is only if you want to keep a characters voice consistent.

Travis Davids

52,424 görüntüleme • 4 ay önce

This is a serious shake-up for traditional VFX studios. A 15-second sequence like this once required months of coordination, large teams, complex simulations, and heavy budgets. Seedance 2.0 produced it in under a minute – for roughly 15-20 cents. When time drops from months to seconds and costs fall from millions to pocket change, the entire production model starts to look very different.
0:15

This is a serious shake-up for traditional VFX studios. A 15-second sequence like this once required months of coordination, large teams, complex simulations, and heavy budgets. Seedance 2.0 produced it in under a minute – for roughly 15-20 cents. When time drops from months to seconds and costs fall from millions to pocket change, the entire production model starts to look very different.

Aleena Amir

96,077 görüntüleme • 5 ay önce

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! 👇
0:15

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

37,946 görüntüleme • 1 ay önce

Depth video workflows have been getting a lot of attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and sensitive-content risks. 2. It preserves the original motion, timing, and spatial structure. This separates motion extraction from visual generation, allowing you to recreate the movement with better models and any reference character. We’ve also launched a free online tool that converts regular videos into depth videos—no local setup required: In the example below, we converted a dance video from Douyin into a depth video, then regenerated it with a reference character using Seedance 2.0. The original choreography and timing are preserved, while the lighting adapts naturally to the new character and scene.
0:14

Depth video workflows have been getting a lot of attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and sensitive-content risks. 2. It preserves the original motion, timing, and spatial structure. This separates motion extraction from visual generation, allowing you to recreate the movement with better models and any reference character. We’ve also launched a free online tool that converts regular videos into depth videos—no local setup required: In the example below, we converted a dance video from Douyin into a depth video, then regenerated it with a reference character using Seedance 2.0. The original choreography and timing are preserved, while the lighting adapts naturally to the new character and scene.

underwood

15,968 görüntüleme • 15 gün önce

Teenage Parkour Runner 🏃‍♂️ Made with Seedance 2.0 by Yapper Prompt : Generate a video: "Use the person from the reference image as the main character. Preserve the same face, hairstyle, age appearance, body type, skin tone, and clothing style from the reference image throughout the entire video. Maintain strong facial consistency and character identity across all shots." 0.0s – 1.5s Use the person from the reference image as the main character. Preserve the same face, hairstyle, age appearance, body type, skin tone, and clothing style from the reference image throughout the entire video. Maintain strong facial consistency and character identity across all shots. Wide aerial shot of a dense modern city at sunset. The teenage parkour runner sprints across a rooftop. Golden sunlight reflects from skyscraper windows. Dynamic drone chase camera from behind. 1.5s – 3.0s The runner accelerates and vaults over rooftop railings and obstacles with expert parkour technique. Low-angle tracking camera emphasizes speed, agility, and athletic movement. 3.0s – 4.5s The runner leaps across a rooftop gap high above the city streets. Cinematic orbit camera during the jump, realistic physics, smooth landing. 4.5s – 6.0s The runner performs a wall-run up the side of a building, grabs a ledge, and pulls upward. Close-up action shot showing determination and fluid movement. 6.0s – 7.5s The runner rapidly climbs a vertical maintenance ladder toward higher rooftops. Camera tilts upward, revealing the glowing sunset skyline. 7.5s – 9.0s The runner emerges onto a higher rooftop and performs a final long jump toward the tallest skyscraper. Epic cinematic perspective, realistic motion, dramatic scale. 9.0s – 10.0s The runner reaches the summit of the tallest building and stands at the edge overlooking the illuminated city. The camera pulls back into a massive aerial shot as the sunset paints the skyline with orange, pink, and purple colors. Epic, inspirational ending. Style: Ultra-realistic, cinematic parkour action, golden hour lighting, realistic human motion, dynamic drone cinematography, high detail, film-quality color grading, volumetric lighting, atmospheric haze, 4K, smooth motion blur, consistent character identity from reference image. Negative Prompt: face changes, identity drift, different person, inconsistent clothing, blurry face, distorted anatomy, extra limbs, unrealistic parkour, floating, physics errors, low quality, cartoon, anime, CGI look, frame glitches, watermark, logo, text, camera shake, motion artifacts, 9:16.
0:15

Teenage Parkour Runner 🏃‍♂️ Made with Seedance 2.0 by Yapper Prompt : Generate a video: "Use the person from the reference image as the main character. Preserve the same face, hairstyle, age appearance, body type, skin tone, and clothing style from the reference image throughout the entire video. Maintain strong facial consistency and character identity across all shots." 0.0s – 1.5s Use the person from the reference image as the main character. Preserve the same face, hairstyle, age appearance, body type, skin tone, and clothing style from the reference image throughout the entire video. Maintain strong facial consistency and character identity across all shots. Wide aerial shot of a dense modern city at sunset. The teenage parkour runner sprints across a rooftop. Golden sunlight reflects from skyscraper windows. Dynamic drone chase camera from behind. 1.5s – 3.0s The runner accelerates and vaults over rooftop railings and obstacles with expert parkour technique. Low-angle tracking camera emphasizes speed, agility, and athletic movement. 3.0s – 4.5s The runner leaps across a rooftop gap high above the city streets. Cinematic orbit camera during the jump, realistic physics, smooth landing. 4.5s – 6.0s The runner performs a wall-run up the side of a building, grabs a ledge, and pulls upward. Close-up action shot showing determination and fluid movement. 6.0s – 7.5s The runner rapidly climbs a vertical maintenance ladder toward higher rooftops. Camera tilts upward, revealing the glowing sunset skyline. 7.5s – 9.0s The runner emerges onto a higher rooftop and performs a final long jump toward the tallest skyscraper. Epic cinematic perspective, realistic motion, dramatic scale. 9.0s – 10.0s The runner reaches the summit of the tallest building and stands at the edge overlooking the illuminated city. The camera pulls back into a massive aerial shot as the sunset paints the skyline with orange, pink, and purple colors. Epic, inspirational ending. Style: Ultra-realistic, cinematic parkour action, golden hour lighting, realistic human motion, dynamic drone cinematography, high detail, film-quality color grading, volumetric lighting, atmospheric haze, 4K, smooth motion blur, consistent character identity from reference image. Negative Prompt: face changes, identity drift, different person, inconsistent clothing, blurry face, distorted anatomy, extra limbs, unrealistic parkour, floating, physics errors, low quality, cartoon, anime, CGI look, frame glitches, watermark, logo, text, camera shake, motion artifacts, 9:16.

Zar⭕on

25,900 görüntüleme • 2 ay önce

Seedance 2.0 has the most accurate character swap available in my opinion. It's managed to maintain complete scene continuity. I can't see anything else that has changed in the entire scene. It's also maintained accurate camera motion and character movement and just swapped out the character as instructed. The best way to see accuracy is to showcase both clips. All you have to do is upload the video you want to replace the character in as an OMNI Reference and an image of the character to replace them with and type in the following prompt and adjust as needed: Keep the scene the same but just swap the women with the grey shirt with a completely different person, it's the black female in Image1 wearing a dark blue knitted sweater. #CapCutSeedance2 CapCut
0:15

Seedance 2.0 has the most accurate character swap available in my opinion. It's managed to maintain complete scene continuity. I can't see anything else that has changed in the entire scene. It's also maintained accurate camera motion and character movement and just swapped out the character as instructed. The best way to see accuracy is to showcase both clips. All you have to do is upload the video you want to replace the character in as an OMNI Reference and an image of the character to replace them with and type in the following prompt and adjust as needed: Keep the scene the same but just swap the women with the grey shirt with a completely different person, it's the black female in Image1 wearing a dark blue knitted sweater. #CapCutSeedance2 CapCut

Travis Davids

23,267 görüntüleme • 4 ay önce

New path control system in Seedance 2.0 just cooked Hollywood... - You draw the camera move by hand. - Scribble a red line across a still image - Feed it to the model and it flies the shot along that exact route. Then it erases the line so it never shows up in the footage. The marker stroke becomes the flight path. Prompts below on Unsora AI👇
0:14

New path control system in Seedance 2.0 just cooked Hollywood... - You draw the camera move by hand. - Scribble a red line across a still image - Feed it to the model and it flies the shot along that exact route. Then it erases the line so it never shows up in the footage. The marker stroke becomes the flight path. Prompts below on Unsora AI👇

Rez Karim

111,562 görüntüleme • 2 ay önce

the fact that i can take an image of a room and turn it into a 3d model in one shot is actually insane this took like 30 seconds from image to 3d model
0:18

the fact that i can take an image of a room and turn it into a 3d model in one shot is actually insane this took like 30 seconds from image to 3d model

Jan

131,884 görüntüleme • 9 ay önce

Kling 3 is a remarkable model. This was a one-shot generation from an input image and text prompts - the model retained character, scene, and voice consistency across cuts 🤯 IMO this is a step-function change in capabilities. If you haven't tried it yet, head over to fal!
0:12

Kling 3 is a remarkable model. This was a one-shot generation from an input image and text prompts - the model retained character, scene, and voice consistency across cuts 🤯 IMO this is a step-function change in capabilities. If you haven't tried it yet, head over to fal!

Justine Moore

16,571 görüntüleme • 6 ay önce