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AI motion capture just got scary good Kling 3.0 upgraded motion control that keeps your character identical across a 5-minute sequence -> same face, same walk, same everything Hollywood-grade stability finally here Here's how: credits phil.franco

36,304 views • 6 months ago •via X (Twitter)

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Made with kling 3.0 on Higgsfield AI 🧩 Video Prompt : Use the uploaded reference image as the exact identity reference for the subject. Create a hyper-realistic live IPL television broadcast crowd-shot sequence during a high-energy playoff cricket match in a packed Indian stadium. The subject from the reference image must remain fully consistent throughout: same face, same hairstyle, same outfit, same skin tone, same lighting, same seating position, and same crowd environment. Preserve identity accuracy strongly across the entire sequence. The video should feel exactly like a genuine Star Sports IPL crowd cutaway captured during a real live match — NOT cinematic, NOT influencer-style, NOT vlog-style. Show realistic live broadcast camera behavior: quick crowd cutaways, natural stadium lighting, authentic TV zoom lens movement, slight camera shake, realistic audience reactions, energetic IPL atmosphere, LED advertisement boards, match scoreboard overlays, cheering fans around the subject. The subject should react naturally to the match: smiling, clapping, looking tense during close moments, celebrating boundaries/wickets, and occasionally looking toward the field. Maintain realistic Indian stadium ambience with thousands of spectators, team jerseys, flags, chants, floodlights, and authentic IPL playoff energy. Ultra realistic skin texture, natural motion, realistic hair movement, accurate facial consistency, broadcast-quality detail, shallow depth of field, true live sports telecast aesthetic, 4K realism, highly detailed crowd environment. Prompt: cinematic lighting, music-video style, slow motion, overacting, beauty filter, influencer aesthetic, vlog framing, AI face distortion, cartoon look, unrealistic expressions, fantasy colors, excessive blur, duplicate faces, identity drift, studio lighting, posed acting, fake crowd.

Shahid Wani

139,457 views • 4 months ago

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

86,047 views • 2 months ago

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 views • 28 days ago