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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...

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Larus Canus

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50,904 görüntüleme • 1 ay önce •via X (Twitter)

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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 👇
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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 👇

Larus Canus

107,283 görüntüleme • 1 ay önce

A new stable motion transfer workflow for Seedance 2.0! We built a video-to-depth-video + skeleton-binding Skill based on "Depth-Anything-V2" for motion transfer. Tell your agent to help me create a Skill that converts a normal video into a depth video with skeleton binding. Motion transfer can be achieved with only the following prompt + content description:
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A new stable motion transfer workflow for Seedance 2.0! We built a video-to-depth-video + skeleton-binding Skill based on "Depth-Anything-V2" for motion transfer. Tell your agent to help me create a Skill that converts a normal video into a depth video with skeleton binding. Motion transfer can be achieved with only the following prompt + content description:

Buzzy Now

12,584 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.
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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.

underwood

15,968 görüntüleme • 29 gün önce

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

Larus Canus

117,472 görüntüleme • 1 ay önce

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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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 görüntüleme • 1 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

85,546 görüntüleme • 1 ay önce

OpenArt just launched Seedance 2.0 for Teams and Enterprise It delivers the kind of camera control, reference depth, and cinematic storytelling most AI video tools still struggle to offer You can use up to 9 image references, 3 videos, and 3 audio files to build multi-shot scenes with far more control than a normal prompt 10 prompts worth saving:
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OpenArt just launched Seedance 2.0 for Teams and Enterprise It delivers the kind of camera control, reference depth, and cinematic storytelling most AI video tools still struggle to offer You can use up to 9 image references, 3 videos, and 3 audio files to build multi-shot scenes with far more control than a normal prompt 10 prompts worth saving:

Amira Zairi

40,480 görüntüleme • 4 ay önce

🔥 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. 🎬
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🔥 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 görüntüleme • 29 gün önce

Create a short film like this in just 1 minute with GPT Image 2.0 + Seedance 2.0. GPT Image 2.0 can naturally combine multiple photos into one single image, while Seedance 2.0 can use that image as a reference to automatically separate the scenes, generate a coherent video sequence, and add suitable background music. This workflow greatly improves the overall creative efficiency. When using this method, simply provide the merged image as a reference for Seedance 2.0 and briefly describe each scene with a simple prompt. This can significantly increase the success rate of the final video. All of the above was created on GPT Image Prompt: Seedance Prompt:
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Create a short film like this in just 1 minute with GPT Image 2.0 + Seedance 2.0. GPT Image 2.0 can naturally combine multiple photos into one single image, while Seedance 2.0 can use that image as a reference to automatically separate the scenes, generate a coherent video sequence, and add suitable background music. This workflow greatly improves the overall creative efficiency. When using this method, simply provide the merged image as a reference for Seedance 2.0 and briefly describe each scene with a simple prompt. This can significantly increase the success rate of the final video. All of the above was created on GPT Image Prompt: Seedance Prompt:

Midjourney Sref and prompt Library

40,572 görüntüleme • 4 ay önce

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 👇
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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 görüntüleme • 2 ay önce

Diving into a new hobby? 🧠 With Gemini Deep Research you can build an in-depth & personalized beginner's guide on nearly anything you're curious about. Just select Deep Research from the prompt bar or model picker dropdown to get started and let Gemini do the research for you:
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Diving into a new hobby? 🧠 With Gemini Deep Research you can build an in-depth & personalized beginner's guide on nearly anything you're curious about. Just select Deep Research from the prompt bar or model picker dropdown to get started and let Gemini do the research for you:

Google Gemini

32,143 görüntüleme • 1 yıl önce

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.
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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

31,955 görüntüleme • 1 ay önce

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.
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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 görüntüleme • 10 gün önce

Seedance 2 ile dövüş sanatları videosu:🥊 1. Referans combat choreography sheet’i yükle 📄 2. Karakter / savaşçı görselini yükle 🖤 3. Hareketleri tek promptla akıt 🎬 9 adımlık dövüş planı ➜ kesintisiz aksiyon videosu 🔥 Guard stance → jab → cross punch → spinning kick finish 🚀 Prompt takibi gerçekten başka seviye. Prompt: 👇 Create a seamless 15-second women’s combat sports choreography video using the uploaded 9-step martial arts reference sheet only for movement order and body-action structure. REFERENCE IMAGE: Use the 9-step martial arts sheet only as choreography reference. Do not recreate the sheet, text, arrows, numbers, or panel layout. MOVEMENT STYLE: Smooth, athletic, feminine, stylish, and continuous. The routine should feel like a choreographed martial arts flow, not separate tutorial poses. Every move should naturally connect to the next.
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Seedance 2 ile dövüş sanatları videosu:🥊 1. Referans combat choreography sheet’i yükle 📄 2. Karakter / savaşçı görselini yükle 🖤 3. Hareketleri tek promptla akıt 🎬 9 adımlık dövüş planı ➜ kesintisiz aksiyon videosu 🔥 Guard stance → jab → cross punch → spinning kick finish 🚀 Prompt takibi gerçekten başka seviye. Prompt: 👇 Create a seamless 15-second women’s combat sports choreography video using the uploaded 9-step martial arts reference sheet only for movement order and body-action structure. REFERENCE IMAGE: Use the 9-step martial arts sheet only as choreography reference. Do not recreate the sheet, text, arrows, numbers, or panel layout. MOVEMENT STYLE: Smooth, athletic, feminine, stylish, and continuous. The routine should feel like a choreographed martial arts flow, not separate tutorial poses. Every move should naturally connect to the next.

ai.gezgini

18,033 görüntüleme • 4 ay önce

GPT image 2.0 combined with Seedance 2.0 is changing how animated videos are created. With a single prompt like the one below , AI can generate an entire storyboard and turn it into a cinematic 2D animation, complete with expressive characters, smooth motion, realistic physics, comedy, and perfectly timed action.
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GPT image 2.0 combined with Seedance 2.0 is changing how animated videos are created. With a single prompt like the one below , AI can generate an entire storyboard and turn it into a cinematic 2D animation, complete with expressive characters, smooth motion, realistic physics, comedy, and perfectly timed action.

FATHELA ESQ

47,447 görüntüleme • 1 ay önce

🤯 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 👇
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🤯 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

46,485 görüntüleme • 1 ay önce

In this setup, a Blender blockout provides the motion and composition while a reference image guides the environment, subject, and textures with MiniMax H3. With open weights, you can previs the final look locally for free or run it on Comfy Cloud. To try this workflow, link below 👇
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In this setup, a Blender blockout provides the motion and composition while a reference image guides the environment, subject, and textures with MiniMax H3. With open weights, you can previs the final look locally for free or run it on Comfy Cloud. To try this workflow, link below 👇

ComfyUI

32,253 görüntüleme • 4 gün önce

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.
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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 görüntüleme • 2 ay önce

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.
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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 görüntüleme • 1 yıl önce

AI video is moving beyond "generate a clip". We're entering the era of directing entire scenes. With MiniMax H3 on Magnific, you can combine: → Text → Images → Videos → Audio in a single workflow. Then control: → Character → Camera → Motion → Sound with far more consistency than a prompt alone. That's what makes this model interesting. 50% OFF until September 1.
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AI video is moving beyond "generate a clip". We're entering the era of directing entire scenes. With MiniMax H3 on Magnific, you can combine: → Text → Images → Videos → Audio in a single workflow. Then control: → Character → Camera → Motion → Sound with far more consistency than a prompt alone. That's what makes this model interesting. 50% OFF until September 1.

Bishal Nandi

53,240 görüntüleme • 16 gün önce