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SAME CHARACTER. TEN SECONDS. CAMERA NEVER STOPS MOVING. that was the wall for years. not resolution, not lighting. identity. one good shot, then your character turns into a different person by second two. it holds now: → same through the whole push-in → camera weaving, never locked off →... show more

marcus

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13,171 次观看 • 3 个月前 •via X (Twitter)

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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

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

11,365 次观看 • 4 个月前

MiniMax H3 vs Seedance 2.0 same prompt, same reference character, completely different fight scenes. 🔥 tested both on Pollo AI. one felt smoother on motion, the other sharper on character consistency. honestly, the gap surprised me. check the prompt and reference character below 👇 which one handled the action better in your eyes?

MiniMax H3 vs Seedance 2.0 same prompt, same reference character, completely different fight scenes. 🔥 tested both on Pollo AI. one felt smoother on motion, the other sharper on character consistency. honestly, the gap surprised me. check the prompt and reference character below 👇 which one handled the action better in your eyes?

Muhammad Ali

22,675 次观看 • 2 个月前

📖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.

📖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 次观看 • 3 个月前

Every AI video creator has this nightmare: You generate a character with one prompt… but in the next shot, she’s a different person. Different face. Different skin. Different eyes. You can’t tell a story like that. BACH — the world’s first Industrial-Grade AI video engine — finally fixes it. Locked Character Consistency. Same woman. Multiple shots. Multiple angles. Zero morphing. Watch this 👇

Every AI video creator has this nightmare: You generate a character with one prompt… but in the next shot, she’s a different person. Different face. Different skin. Different eyes. You can’t tell a story like that. BACH — the world’s first Industrial-Grade AI video engine — finally fixes it. Locked Character Consistency. Same woman. Multiple shots. Multiple angles. Zero morphing. Watch this 👇

Robert Scoble

38,741 次观看 • 5 个月前

Your creative workflow will never be the same! Introducing Nano Banana 🍌 Google’s most advanced image-generation model yet! One-shot editing. Photorealistic results. Flawless character consistency. Try it today in ImagineArt Studio.

Your creative workflow will never be the same! Introducing Nano Banana 🍌 Google’s most advanced image-generation model yet! One-shot editing. Photorealistic results. Flawless character consistency. Try it today in ImagineArt Studio.

ImagineArt

381,490 次观看 • 1 年前

i genuinely cannot believe more people aren't talking about this there are AI character accounts sitting at hundreds of thousands of followers right now one fictional character placed into different situations every video and the audience keeps coming back because they want to see what happens next that narrative pull: "what does this character do in this scenario" is one of the most powerful retention mechanics in short form content and it costs almost nothing to run one consistent character. one content format. posted every day. and when you attach a glitchy ecom offer to a page with that kind of following the commissions come from an audience that trusts the character before the offer even appears here's how you start → build the character concept, name, personality, visual identity, the situations they'll be placed in → generate a consistent character sheet so the face and outfit stay the same every video → write scene ideas and movement plans for each video → turn each scene into a detailed video prompt, actions, camera angles, timing → animate using your preferred AI video tool → keep the character visually identical across every episode start with one character and ten videos the monetisation builds from there ->affiliate commissions ->brand deals and an audience that compounds every week the character keeps showing up you don't need a paid course to learn the process

Laur

76,623 次观看 • 21 天前

Face swap? Not enough. Now it’s Character Swap time. 🔥 With Akool, you can swap not just the face but the entire character. Step into any scene, become anyone, and remake your own cinematic world in seconds. Your movie. Your story. Your character. Try Character Swap on Akool now.

Face swap? Not enough. Now it’s Character Swap time. 🔥 With Akool, you can swap not just the face but the entire character. Step into any scene, become anyone, and remake your own cinematic world in seconds. Your movie. Your story. Your character. Try Character Swap on Akool now.

Akool Inc

431,803 次观看 • 10 个月前

GPT Image 2.0 + Seedance 2.0 is a powerful combo for game scene creation. Generate the character UI, loading screen, HUD, and cinematic gameplay look with GPT Image 2.0, then use Seedance 2.0 to turn it into a smooth one-shot game demo video.

GPT Image 2.0 + Seedance 2.0 is a powerful combo for game scene creation. Generate the character UI, loading screen, HUD, and cinematic gameplay look with GPT Image 2.0, then use Seedance 2.0 to turn it into a smooth one-shot game demo video.

BudgetPixel AI

730,287 次观看 • 5 个月前

MiniMax H3 Character Detail Montage This prompt turns your character reference into a fast-paced detail montage: clean background, dynamic movement, sharp cuts, and explosive camera moves. The face stays hidden until the final shot. MiniMax H3 prompt in the replies.

MiniMax H3 Character Detail Montage This prompt turns your character reference into a fast-paced detail montage: clean background, dynamic movement, sharp cuts, and explosive camera moves. The face stays hidden until the final shot. MiniMax H3 prompt in the replies.

Kōda

516,029 次观看 • 1 个月前

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.

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

86,241 次观看 • 3 个月前

making VIRAL AI characters is SO easy it feels illegal. and they're pulling 100M+ views... even on BRAND NEW accounts. here's why it WORKS: → weird, bizarre characters you can NEVER forget → REAL motion copied from videos that already went viral copy this exact workflow: STEP 1: find a funny or strange character on pinterest, turn it into a character sheet. ask chatgpt this (and attach the image): "create a realistic character sheet of this person using 3 panels against a studio background: full-body front view with no head, full-body back view with the head visible, close-up shot of the face" STEP 2: download any viral video you love, swap the person using Higgsfield Genjutsu. use this prompt: "replace the person in @ video1 with the character from @ image1. keep the same motion, camera movement, color grading, lighting, framing, and overall video quality." DONE. that's literally it.

ViralOps

805,324 次观看 • 23 天前

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.

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

120,346 次观看 • 1 个月前

Seedance 2.5 - Prompt Share ✨ This prompt turns any character into a dance video, creating choreography that matches the character’s style and personality. I tried it with my character, and it should work with any character. Prompt in thread 🧵

Seedance 2.5 - Prompt Share ✨ This prompt turns any character into a dance video, creating choreography that matches the character’s style and personality. I tried it with my character, and it should work with any character. Prompt in thread 🧵

Kiki

50,198 次观看 • 1 个月前

This AI-generated girl is turning ordinary bowling clips into viral content. The formula is ridiculously simple: She walks into a bowling alley. Picks up a ball. Throws it. Smiles for the camera. But something about the character, the setting, and the realistic movement makes people stop scrolling. One video gets attention. Another gets shared. Then another crosses millions of views. And the crazy part? There’s no actress. No bowling trip. No filming crew. No expensive production. The entire character and every scene can be created with AI. Change the outfit. Change the location. Change the activity. Keep the same face. Suddenly you have an endless content machine built around one recognizable character. The real opportunity isn’t creating one viral AI video. It’s creating a character people recognize - and then putting her everywhere. Bowling today. Beach tomorrow. Gym next week. Same girl. Different scene. Millions of possible videos. And once the audience is there, the views can turn into brand deals, affiliate revenue, product promotions and more. AI isn’t just generating images anymore. It’s creating characters that can become entire media brands.

genuenci

140,143 次观看 • 2 个月前

Seedance 2.0 4K is now live in ElevenCreative. Native 4K resolution, character consistency across shots, and reference-based style control - precision at every frame, across every shot.

Seedance 2.0 4K is now live in ElevenCreative. Native 4K resolution, character consistency across shots, and reference-based style control - precision at every frame, across every shot.

ElevenCreative

371,662 次观看 • 4 个月前

AI video consistency starts before motion. For this 15s cooking short, we first generated a clean character storyboard in PixVerse. Then used it as reference for the video. Same character. Clear story beats. Camera direction. Motion details. RT + Follow + Reply = Storyboard Workflow

AI video consistency starts before motion. For this 15s cooking short, we first generated a clean character storyboard in PixVerse. Then used it as reference for the video. Same character. Clear story beats. Camera direction. Motion details. RT + Follow + Reply = Storyboard Workflow

PixVerse

22,348 次观看 • 5 个月前

GPT Image 2.0 + Seedance 2.0 = a powerful dance workflow First, use GPT Image 2.0 to create your main character image and the dance moves image. Then feed them into Seedance 2.0 to turn them into a full dance video. From character design → choreography → motion. All in one smooth pipeline.

GPT Image 2.0 + Seedance 2.0 = a powerful dance workflow First, use GPT Image 2.0 to create your main character image and the dance moves image. Then feed them into Seedance 2.0 to turn them into a full dance video. From character design → choreography → motion. All in one smooth pipeline.

BudgetPixel AI

564,222 次观看 • 6 个月前

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.

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

110,239 次观看 • 3 个月前

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

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

23,535 次观看 • 6 个月前

Still experimenting with using a single visual production graph as the anchor for multi-shot storytelling in Seedance 2.0 • Paired it with a structured text prompt to define sequence, camera moves, and character consistency • Used explicit spatial rules (door hierarchy + movement direction) to prevent scene resets and continuity errors

Still experimenting with using a single visual production graph as the anchor for multi-shot storytelling in Seedance 2.0 • Paired it with a structured text prompt to define sequence, camera moves, and character consistency • Used explicit spatial rules (door hierarchy + movement direction) to prevent scene resets and continuity errors

Heather Cooper

64,879 次观看 • 5 个月前