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🤯 Found another great use case for Depth today! I tried recreating this fashion transformation video with my own character, but the original character kept showing up. 🌟 So I switched the reference video to Depth, and it worked way better! Same motion and timing, but now I can... show more

Larus Canus

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117,472 Aufrufe • vor 1 Monat •via X (Twitter)

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🤯 I didn’t expect Depth to track rooftop parkour this cleanly! Used a fast climbing clip as the motion reference, then swapped in a student with twin tails and a backpack on a coastal school rooftop. The running path, wall contact, jumps, landings, and full-body continuity all hold together surprisingly well. 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Depth opens up a lot more possibilities for parkour, climbing, martial arts, dance, and other complex full-body motion. Workflow + Prompt below 👇

🤯 I didn’t expect Depth to track rooftop parkour this cleanly! Used a fast climbing clip as the motion reference, then swapped in a student with twin tails and a backpack on a coastal school rooftop. The running path, wall contact, jumps, landings, and full-body continuity all hold together surprisingly well. 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Depth opens up a lot more possibilities for parkour, climbing, martial arts, dance, and other complex full-body motion. Workflow + Prompt below 👇

Larus Canus

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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.

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

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🤯 Depth videos perfectly solve false flags on reference videos — and can perfectly recreate both martial arts and dance! Turned a Guan Dao martial arts clip into a Depth motion reference, swapped the fighter for a market auntie, and moved the scene to a T-junction inside a local market 🤣 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. This goes way beyond trending dances. Martial arts, polearms, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

🤯 Depth videos perfectly solve false flags on reference videos — and can perfectly recreate both martial arts and dance! Turned a Guan Dao martial arts clip into a Depth motion reference, swapped the fighter for a market auntie, and moved the scene to a T-junction inside a local market 🤣 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. This goes way beyond trending dances. Martial arts, polearms, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

Larus Canus

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🤯 Depth can now recreate complex climbing and parkour motion this cleanly! Turned a fast climbing clip into a Depth motion reference, then swapped in a 20-year-old twin-tail student with a backpack while keeping the same concrete structure. Wall contact, weight shifts, jumps, climbing paths, and full-body movement all stay surprisingly clean and consistent! 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Dance, martial arts, climbing, parkour, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

🤯 Depth can now recreate complex climbing and parkour motion this cleanly! Turned a fast climbing clip into a Depth motion reference, then swapped in a 20-year-old twin-tail student with a backpack while keeping the same concrete structure. Wall contact, weight shifts, jumps, climbing paths, and full-body movement all stay surprisingly clean and consistent! 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Dance, martial arts, climbing, parkour, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

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New fal workflow: video restyling with Video Depth Anything and Seedance 2.0 - Restyle the first frame into your target look - Extract consistent depth across the source video - Use that depth to guide Seedance 2.0 through the full sequence - Preserve the original motion and composition while transforming the visual style A simple workflow for turning existing footage into stylized video.

New fal workflow: video restyling with Video Depth Anything and Seedance 2.0 - Restyle the first frame into your target look - Extract consistent depth across the source video - Use that depth to guide Seedance 2.0 through the full sequence - Preserve the original motion and composition while transforming the visual style A simple workflow for turning existing footage into stylized video.

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For the facial animations in my game, I use a technique that yields results similar to L.A. Noire. To keep it simple: I first generate a facial animation video using AI like LTX 2. Then, I create a video depth map from that animation and project it onto a face mask. Using vertex displacement, the depth map dynamically deforms the face as the character speaks. The result looks good in standalone VR. I actually created this workflow long time ago:

For the facial animations in my game, I use a technique that yields results similar to L.A. Noire. To keep it simple: I first generate a facial animation video using AI like LTX 2. Then, I create a video depth map from that animation and project it onto a face mask. Using vertex displacement, the depth map dynamically deforms the face as the character speaks. The result looks good in standalone VR. I actually created this workflow long time ago:

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I made a new Sailor Moon live-action transformation video. Hope you like it! Which character should I make next? Tell me in the comments. The full workflow, prompt, and Canvas share link are in the comments.

I made a new Sailor Moon live-action transformation video. Hope you like it! Which character should I make next? Tell me in the comments. The full workflow, prompt, and Canvas share link are in the comments.

underwood

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Tried Higgsfield's motion control feature today and genuinely impressed. All I did was select a reference video and give it my own character sheet, and the output followed the reference motion almost perfectly, just with my character in it. Makes replicating a specific camera move or movement so much easier without having to prompt it from scratch. Worth trying if you haven't yet. Higgsfield AI 🧩

Tried Higgsfield's motion control feature today and genuinely impressed. All I did was select a reference video and give it my own character sheet, and the output followed the reference motion almost perfectly, just with my character in it. Makes replicating a specific camera move or movement so much easier without having to prompt it from scratch. Worth trying if you haven't yet. Higgsfield AI 🧩

Shara I Ai Video Creator

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Just generated this clip in 30 seconds 1. record a vertical video 2. use MakeUGC to generate an AI character 3. use Elevenlabs to convert my voice to match the girl's voice 4. replace the AI character with me on the video comment "Motion" and I'll send you the full workflow

Just generated this clip in 30 seconds 1. record a vertical video 2. use MakeUGC to generate an AI character 3. use Elevenlabs to convert my voice to match the girl's voice 4. replace the AI character with me on the video comment "Motion" and I'll send you the full workflow

Cas.Fyn

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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.

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

85,768 Aufrufe • vor 1 Monat

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.

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So I'm planning on developing a wheelchair-bound character for my fighting game, and this is the one other fighting game character I can use to reference. What

So I'm planning on developing a wheelchair-bound character for my fighting game, and this is the one other fighting game character I can use to reference. What

Rad Chad

484,729 Aufrufe • vor 1 Jahr

MiniMax H3 Character Introduction Template So it looks like MiniMax H3 understands parameters. You can rewrite the text parameters in the prompt to match your character and use it that way. I’m sharing the character sheet and the prompt I used below.

MiniMax H3 Character Introduction Template So it looks like MiniMax H3 understands parameters. You can rewrite the text parameters in the prompt to match your character and use it that way. I’m sharing the character sheet and the prompt I used below.

Kōda

178,176 Aufrufe • vor 23 Tagen

🤯 K-pop meets a full-on water obstacle challenge! 🌊 Made with Seedance in a Korean variety show style Full workflow: 1. 4:3 character sheet for the girl, swimsuit and expressions 2. 3:1 wide course image for the pool set, audience and obstacles 3. Seedance image-to-video for the full run and final splash From the confident start to the final fall, the character and scene consistency held up really well! Workflow + Prompt in the comments 👇

🤯 K-pop meets a full-on water obstacle challenge! 🌊 Made with Seedance in a Korean variety show style Full workflow: 1. 4:3 character sheet for the girl, swimsuit and expressions 2. 3:1 wide course image for the pool set, audience and obstacles 3. Seedance image-to-video for the full run and final splash From the confident start to the final fall, the character and scene consistency held up really well! Workflow + Prompt in the comments 👇

Larus Canus

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kling 2.6 motion control is actually insane for AI ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scale

kling 2.6 motion control is actually insane for AI ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scale

Miko

29,467 Aufrufe • vor 8 Monaten

MiniMax H3 is now on Magnific and honestly, there’s a lot you can do with it. You can mix text, images, videos and audio in a single prompt up to 9 images, 3 videos and 3 audio references. Start creating now: It can generate up to 15s of 2K video with synced sound, including voice, music and effects. And you can go beyond generation too: edit clips, remove objects, transfer motion, and control the camera, character and voice. But the multi-reference workflow is probably my favorite. Give it your product, character, environment and motion references, and H3 pulls everything together. It feels like a much easier way to go from an idea to an actual finished video.

MiniMax H3 is now on Magnific and honestly, there’s a lot you can do with it. You can mix text, images, videos and audio in a single prompt up to 9 images, 3 videos and 3 audio references. Start creating now: It can generate up to 15s of 2K video with synced sound, including voice, music and effects. And you can go beyond generation too: edit clips, remove objects, transfer motion, and control the camera, character and voice. But the multi-reference workflow is probably my favorite. Give it your product, character, environment and motion references, and H3 pulls everything together. It feels like a much easier way to go from an idea to an actual finished video.

Kalsoom (ghotai )

47,888 Aufrufe • vor 16 Tagen

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

underwood

32,883 Aufrufe • vor 3 Monaten

This opens up a completely new workflow for artists when it comes to shooting music videos. Take one performance and place it into multiple locations from multiple angles. By combining Kling 2.6 Motion Control with our Multi-Angle Fashion Shoot agent, we generate multiple angles from a single reference image and use them to drive the performance. Full workflow + agent below 👇

This opens up a completely new workflow for artists when it comes to shooting music videos. Take one performance and place it into multiple locations from multiple angles. By combining Kling 2.6 Motion Control with our Multi-Angle Fashion Shoot agent, we generate multiple angles from a single reference image and use them to drive the performance. Full workflow + agent below 👇

GLIF

58,538 Aufrufe • vor 8 Monaten

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

23,267 Aufrufe • vor 4 Monaten