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After extracting the depth video, the effect obtained by video referencing is very good. 1. Prepare the reference video 2. Extract the depth video 3. Prepare the character reference images 4. Input the extracted depth video and character reference images into seedance2, and you will get perfect results Complete... show more

SJinn Agent

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91,622 次观看 • 1 个月前 •via X (Twitter)

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1. Reference Video -> Convert to Depth Map 2. Depth Map -> Seedance 2.0 (Reference Video) 3. Add Reference Image 4. Set Duration / Resolution / Aspect 5. Pass Audio from Reference Video -> Final 6. Hit Run

1. Reference Video -> Convert to Depth Map 2. Depth Map -> Seedance 2.0 (Reference Video) 3. Add Reference Image 4. Set Duration / Resolution / Aspect 5. Pass Audio from Reference Video -> Final 6. Hit Run

Purz.ai

31,926 次观看 • 1 个月前

🤯 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 use my own character and completely new outfits! Quick workflow in the comments Full Depth workflow in the quoted post 👇

🤯 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 use my own character and completely new outfits! Quick workflow in the comments Full Depth workflow in the quoted post 👇

Larus Canus

117,472 次观看 • 1 个月前

Guys, Depth Map Control now live on Mini Apps The workflow is quite easy: reference video -> depth map video -> reference image + depthvideo -> a video that follows motion and space more precisely. RT + Follow + Reply = 150 Credits in DMs (72H ONLY)

Guys, Depth Map Control now live on Mini Apps The workflow is quite easy: reference video -> depth map video -> reference image + depthvideo -> a video that follows motion and space more precisely. RT + Follow + Reply = 150 Credits in DMs (72H ONLY)

PixVerse

415,460 次观看 • 1 个月前

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

15,968 次观看 • 1 个月前

🤯 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 👇

Larus Canus

107,283 次观看 • 1 个月前

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.

fal

10,909 次观看 • 1 个月前

Let's create a music video with these characters. Used the Midjourney images directly into Seedance 2.0 and added the track I created in Suno as a blackout video reference. You can find the prompt below.

Let's create a music video with these characters. Used the Midjourney images directly into Seedance 2.0 and added the track I created in Suno as a blackout video reference. You can find the prompt below.

Kōda

32,298 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

85,768 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

116,251 次观看 • 7 天前

Midjourney + GPT Image 2 + Seedance 2.0 - Prompt Share Zero Trace 1. Generate the character. - Midjourney v8.1: hacker girl --ar 2:3 --profile w9b13s1 --stylize 1000 2. Create a character sheet. 3. Create a storyboard using the character sheet as the character identity reference. 4. Create the final video using both the character sheet and storyboard as references. You can find the prompts below.

Kōda

40,012 次观看 • 3 个月前

Turn a simple idea into a storyboard, then generate the video with Seedance 2.0. This ComfyUI workflow uses LLMs to structure prompts into storyboard-ready scenes that define how the video should play out. The storyboard can be paired with reference images and sent directly into Seedance for video generation. To try this workflow, link below 👇

Turn a simple idea into a storyboard, then generate the video with Seedance 2.0. This ComfyUI workflow uses LLMs to structure prompts into storyboard-ready scenes that define how the video should play out. The storyboard can be paired with reference images and sent directly into Seedance for video generation. To try this workflow, link below 👇

ComfyUI

33,988 次观看 • 3 个月前

MiniMax H3 Character Board Animation (Bottom Video) & Character Intro Animation (Top Video) First, we create our character board in GPT Image 2 or Seedream 5 using our character reference image. Then, we use that board as a reference in MiniMax H3 to turn it into an animated character board. Also, you can create another character animation with this board. You can find the prompts below.

MiniMax H3 Character Board Animation (Bottom Video) & Character Intro Animation (Top Video) First, we create our character board in GPT Image 2 or Seedream 5 using our character reference image. Then, we use that board as a reference in MiniMax H3 to turn it into an animated character board. Also, you can create another character animation with this board. You can find the prompts below.

Kōda

17,438 次观看 • 16 天前

Nano Banana 2 now takes video as input 🍌 - Drop a video, get context-aware images out - Thumbnails & infographics generated from your footage - Character and style consistency across frames - Edit outputs with a reference image + prompt Feature available on Runware now

Nano Banana 2 now takes video as input 🍌 - Drop a video, get context-aware images out - Thumbnails & infographics generated from your footage - Character and style consistency across frames - Edit outputs with a reference image + prompt Feature available on Runware now

Runware

14,261 次观看 • 3 个月前

🔥 AI video creation is finally evolving beyond basic motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬

🔥 AI video creation is finally evolving beyond basic motion prompts. Try out Depth Map Control right here: Historically, the hardest part of the process has been maintaining scene consistency: • camera movement • object placement • spatial relationships Thanks to PixVerse Depth Map Control, you can now grab depth data from a reference video and merge it with a fresh image to explore highly directed video generation. The final outcome? Much smoother camera tracking. Highly cohesive visual scenes. It is a powerful workflow that hands creators actual authority over how their AI visuals shift and flow over time. 🎬

Elara Quinn

12,202 次观看 • 1 个月前

Recreating a video's style normally takes hours of editing and iteration. With Wizstar_official Video Reference feature, I simply uploaded a reference video and let AI do the work. The results were surprisingly close to the original feel. 👇

Recreating a video's style normally takes hours of editing and iteration. With Wizstar_official Video Reference feature, I simply uploaded a reference video and let AI do the work. The results were surprisingly close to the original feel. 👇

RAVI KUMAR SAHU

73,895 次观看 • 2 个月前

Seedance 2.0 - Advanced Workflow Series 🧵 7. Storyboard to Video Provide basic storyboards along with a character sheet and environment images. Seedance will create a complete sequence by connecting the shots, with cuts between the different panels. Workflow + Prompts 👇

Seedance 2.0 - Advanced Workflow Series 🧵 7. Storyboard to Video Provide basic storyboards along with a character sheet and environment images. Seedance will create a complete sequence by connecting the shots, with cuts between the different panels. Workflow + Prompts 👇

VoxelPlot

67,015 次观看 • 4 个月前

KAIYA: The Wolf Blade GPT Images 2 + Seedance 2 is an insane combo. Created the entire character sheet with GPT Images… Then turned it into a cinematic video using Seedance. this will change how we create AI videos... Prompts below: ↓

KAIYA: The Wolf Blade GPT Images 2 + Seedance 2 is an insane combo. Created the entire character sheet with GPT Images… Then turned it into a cinematic video using Seedance. this will change how we create AI videos... Prompts below: ↓

ZOYA ✪

36,382 次观看 • 4 个月前

Starting from only a few reference images… Magnific Spaces turned them into a complete cinematic video sequence. The final result keeps the same mood, style, and visual identity surprisingly well across the entire clip. Magnific (formerly Freepik) #Magnific

Starting from only a few reference images… Magnific Spaces turned them into a complete cinematic video sequence. The final result keeps the same mood, style, and visual identity surprisingly well across the entire clip. Magnific (formerly Freepik) #Magnific

NeuraFlow AI

99,293 次观看 • 3 个月前

Create the viral “girlfriend opens the door” video with GPT Image 2 + local MiniMax H3. 1️⃣ Use GenVizu Canvas Mode to generate the character and scene reference images. 2️⃣ Use my ComfyUI node to turn a simple prompt into a structured 6-part H3 prompt. 3️⃣ Wait & Enjoy! ✨

Create the viral “girlfriend opens the door” video with GPT Image 2 + local MiniMax H3. 1️⃣ Use GenVizu Canvas Mode to generate the character and scene reference images. 2️⃣ Use my ComfyUI node to turn a simple prompt into a structured 6-part H3 prompt. 3️⃣ Wait & Enjoy! ✨

leolee

19,847 次观看 • 3 天前

Kling 2.6 Motion Capture is so good. It's a huge leap forward. We've been able to do motion capture with AI for a while, but the quality bump finally made this approach useful. Here are the steps: - Get a reference video - record it or get a stock video - Grab the start frame of that video - Edit it using Nano Banana Pro. Ask to replace character and background. - Select the Kling 2.6 Motion Capture model in the video generator on Freepik (now Magnific) - Upload reference video + set the edited start frame - Video prompt can be simple e.g. "Gandalf dancing"

Kling 2.6 Motion Capture is so good. It's a huge leap forward. We've been able to do motion capture with AI for a while, but the quality bump finally made this approach useful. Here are the steps: - Get a reference video - record it or get a stock video - Grab the start frame of that video - Edit it using Nano Banana Pro. Ask to replace character and background. - Select the Kling 2.6 Motion Capture model in the video generator on Freepik (now Magnific) - Upload reference video + set the edited start frame - Video prompt can be simple e.g. "Gandalf dancing"

Martin LeBlanc

35,274 次观看 • 8 个月前