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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)

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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. 🎬
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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 просмотров • 2 месяцев назад

720p was fine. 1080P changes everything. Seedance 2.0 is live on PixVerse — sharper details, smoother motion, cinema-grade lighting. 72H early access starts now. RT+Follow+Reply=300Creds(72H ONLY)
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720p was fine. 1080P changes everything. Seedance 2.0 is live on PixVerse — sharper details, smoother motion, cinema-grade lighting. 72H early access starts now. RT+Follow+Reply=300Creds(72H ONLY)

PixVerse

33,488 просмотров • 5 месяцев назад

xAI is rolling out support for shareable Imagine templates on the Grok web. Users can now create custom templates with 3 different options - Photo > Video - Photo > Style Edit - Photo > Edit > Video Additionally, the "Image Reference" template type is also in the works.
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xAI is rolling out support for shareable Imagine templates on the Grok web. Users can now create custom templates with 3 different options - Photo > Video - Photo > Style Edit - Photo > Edit > Video Additionally, the "Image Reference" template type is also in the works.

🚨 AI News | TestingCatalog

17,183 просмотров • 5 месяцев назад

STOP WASTING YOUR CREDITS ON 1080P ! Preview Mode is now LIVE on PixVerse Web! Generate in 360p or 540p, then Upscale your favorite shots (4K). Save up to 60% credits! ->Retweet to claim 300 credits!（72H ONLY）
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STOP WASTING YOUR CREDITS ON 1080P ! Preview Mode is now LIVE on PixVerse Web! Generate in 360p or 540p, then Upscale your favorite shots (4K). Save up to 60% credits! ->Retweet to claim 300 credits!（72H ONLY）

PixVerse

249,842 просмотров • 11 месяцев назад

🔥 MiniMax H3 is now live on DomoAI. One multimodal workflow. Multiple ways to create. ✍️ Text → Video 🖼️ Image → Video 🎵 Audio → Video 🎬 Video → Video With Omni Reference, combine up to 9 images, 3 videos, and 3 audio references in a single creation. More creative control. Less credits. 🚀Create with MiniMax H3 on DomoAI： #DomoAI #MiniMaxH3 #GenerativeAI #AIMusicVideo
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🔥 MiniMax H3 is now live on DomoAI. One multimodal workflow. Multiple ways to create. ✍️ Text → Video 🖼️ Image → Video 🎵 Audio → Video 🎬 Video → Video With Omni Reference, combine up to 9 images, 3 videos, and 3 audio references in a single creation. More creative control. Less credits. 🚀Create with MiniMax H3 on DomoAI： #DomoAI #MiniMaxH3 #GenerativeAI #AIMusicVideo

DomoAI official

17,637 просмотров • 1 месяц назад

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

119,753 просмотров • 1 месяц назад

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

86,085 просмотров • 2 месяцев назад

🤯 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

131,723 просмотров • 2 месяцев назад

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
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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 просмотров • 4 месяцев назад

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 просмотров • 2 месяцев назад

🤯 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 👇
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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 👇

Larus Canus

51,786 просмотров • 2 месяцев назад

🤯 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 👇
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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 👇

Larus Canus

24,613 просмотров • 2 месяцев назад

A single image. Cinematic motion. Turn wallpapers into living visuals with Motion Control on BudgetPixel AI. Drop in a reference → control the movement → get a premium ad-grade video in seconds.
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A single image. Cinematic motion. Turn wallpapers into living visuals with Motion Control on BudgetPixel AI. Drop in a reference → control the movement → get a premium ad-grade video in seconds.

BudgetPixel AI

204,103 просмотров • 8 месяцев назад

The frame cuts off half the scene. Cropping again is not the answer. Magic Extend on PixVerse Web outpaints the missing space — subject stays, background fills in. RT+Follow+Reply=300Creds in dm (72H ONLY)
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The frame cuts off half the scene. Cropping again is not the answer. Magic Extend on PixVerse Web outpaints the missing space — subject stays, background fills in. RT+Follow+Reply=300Creds in dm (72H ONLY)

PixVerse

160,640 просмотров • 3 месяцев назад

SEEDREAM 5.0 lite dropped on PixVerse from Day-0. UNLIMITED FOR 365 DAYS for Ultra membership, only on PixVerse. RT+Follow+Reply = 150 Cred (72H ONLY)
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SEEDREAM 5.0 lite dropped on PixVerse from Day-0. UNLIMITED FOR 365 DAYS for Ultra membership, only on PixVerse. RT+Follow+Reply = 150 Cred (72H ONLY)

PixVerse

284,062 просмотров • 7 месяцев назад

🤯 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 👇
0:15

🤯 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

119,251 просмотров • 2 месяцев назад

Digital dreams meet the NFL Node-based AI video workflow for this NFL project by re4ee. Generation, upscale, and motion control in one place. The full workflow is live. Follow + reply "FOOTBALL" to receive a dm of the workflow link.
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Digital dreams meet the NFL Node-based AI video workflow for this NFL project by re4ee. Generation, upscale, and motion control in one place. The full workflow is live. Follow + reply "FOOTBALL" to receive a dm of the workflow link.

TapNow

2,236,202 просмотров • 6 месяцев назад

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

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

underwood

15,968 просмотров • 2 месяцев назад

STOP. HappyHorse goes member-free + 0 credits Until May 7 00:00 PDT — after PixVerse makes it actually happy. RT+Follow+Reply=300Creds(72H ONLY)
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STOP. HappyHorse goes member-free + 0 credits Until May 7 00:00 PDT — after PixVerse makes it actually happy. RT+Follow+Reply=300Creds(72H ONLY)

PixVerse

432,659 просмотров • 5 месяцев назад

The Grok Imagine updates keep accelerating. Reference-to-Video + Video Extend just landed! Live in ComfyUI on Day 0. The generation speed alone makes it worth trying. Reference-to-Video: add up to 7 reference images to generate consistent characters and scenes Video Extend: seamlessly extend any video with smooth, natural motion Workflows and more details about the Grok updates below ⬇️
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The Grok Imagine updates keep accelerating. Reference-to-Video + Video Extend just landed! Live in ComfyUI on Day 0. The generation speed alone makes it worth trying. Reference-to-Video: add up to 7 reference images to generate consistent characters and scenes Video Extend: seamlessly extend any video with smooth, natural motion Workflows and more details about the Grok updates below ⬇️

ComfyUI

34,555 просмотров • 6 месяцев назад