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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

Robin Delta

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

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crazy.. photoshop but for video is here seedance 2.0 now can edit video in layers like ground, sky, bg or objects separately on arcads AI.. and keeps camera and character motion perfectly the same.. this is revolution for film industry here's how to do:

crazy.. photoshop but for video is here seedance 2.0 now can edit video in layers like ground, sky, bg or objects separately on arcads AI.. and keeps camera and character motion perfectly the same.. this is revolution for film industry here's how to do:

el.cine

151,873 views • 5 months ago

The Kling Challenge just got extended till Dec 31 11:59pm PST 🔥 More time. Same $10,000 prize pool. If you’re building cinematic cuts with Kling Omni or 2.6, this is your moment. Smooth motion, locked identity, clean transitions. Let’s see how far you can push Kling AI 🚀

The Kling Challenge just got extended till Dec 31 11:59pm PST 🔥 More time. Same $10,000 prize pool. If you’re building cinematic cuts with Kling Omni or 2.6, this is your moment. Smooth motion, locked identity, clean transitions. Let’s see how far you can push Kling AI 🚀

ImagineArt

1,050,928 views • 9 months ago

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 views • 9 months ago

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 views • 9 months ago

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Miko

61,994 views • 2 months ago

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

25,247 views • 8 months ago

most AI video tools do one thing okay and everything else terribly. I gave Wizstar AI a single photo and it built a full marketing video with lip-synced voiceover, translated it into another language, and kept the same character across every scene. Try it → here's a demo I made by myself and I got surprised: → no product distortion or unwanted changes → smooth motion without lag or deformation → consistent product appearance, details, and branding

most AI video tools do one thing okay and everything else terribly. I gave Wizstar AI a single photo and it built a full marketing video with lip-synced voiceover, translated it into another language, and kept the same character across every scene. Try it → here's a demo I made by myself and I got surprised: → no product distortion or unwanted changes → smooth motion without lag or deformation → consistent product appearance, details, and branding

Muhammad Ayan

56,382 views • 2 months ago

€10,000/month per AI influencer (Aitana López). $8,000–34,000 per sponsored post at the top. Lil Miquela has 2.4M followers and brand deals with Prada, Calvin Klein, Samsung, Dior. she doesn't exist. the new generation runs on a tighter stack: 1. lock a face once. one platform (Apob AI / Higgsfield) keeps it identical across every video. 2. find a viral reference clip in your niche dance, lifestyle, fitness, fashion. 3. swap your AI face onto the original creator's body. 90 seconds per clip. 4. Seedance 2.0 or Kling Motion Control handles the motion. zero animation skills required. 5. post 2–3 times a day across TikTok, Reels, Shorts, Facebook simultaneously. 6. one viral clone = 50,000–500,000 views on a brand new account in a weekend. first dollar in week 2: $9–27 digital product in bio. brand deals in month 3–6 once metrics warm up. portfolio play: same backend runs 3–5 personas in parallel. influencer economy 2024: real face, real fatigue, one bad day kills a brand deal. influencer economy 2026: a face that never ages, never cancels, never burns out. the next million-follower account on your FYP probably wasn't born. it was prompted, swapped, and shipped while the operator slept.

dunik

90,686 views • 4 months ago

Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Mike Futia

25,607 views • 7 months ago

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.

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

THIS IS THE AI VIDEO TEST MOST PEOPLE MISS At first, this just looks like a normal POV clip from a crowded carnival booth: plush bears hanging above, people moving through the frame, and a girl at the counter paying with her phone. But the real test starts when she gets covered. A man carrying a child walks across the lens and fully hides her, then seconds later another group passes through and blocks her again. She disappears twice, and both times comes back exactly the same: same position, same posture, same payment motion, same phone, same hand, same angle That is where AI video usually exposes itself. A clean shot is easy to keep stable, but restoring a hidden subject after full obstruction without changing the face, hands, outfit, timing or object placement is much harder. Here, the continuity holds. No face drift, no hand glitch, no outfit shift, no phone jump, no soft reset after the frame clears. And the wild part: none of it was real. Not the booth, not the plush toys, not the strangers crossing at the perfect moment. AI video is starting to handle the messy, random interruptions that make footage feel real

THIS IS THE AI VIDEO TEST MOST PEOPLE MISS At first, this just looks like a normal POV clip from a crowded carnival booth: plush bears hanging above, people moving through the frame, and a girl at the counter paying with her phone. But the real test starts when she gets covered. A man carrying a child walks across the lens and fully hides her, then seconds later another group passes through and blocks her again. She disappears twice, and both times comes back exactly the same: same position, same posture, same payment motion, same phone, same hand, same angle That is where AI video usually exposes itself. A clean shot is easy to keep stable, but restoring a hidden subject after full obstruction without changing the face, hands, outfit, timing or object placement is much harder. Here, the continuity holds. No face drift, no hand glitch, no outfit shift, no phone jump, no soft reset after the frame clears. And the wild part: none of it was real. Not the booth, not the plush toys, not the strangers crossing at the perfect moment. AI video is starting to handle the messy, random interruptions that make footage feel real

Performance

649,534 views • 12 days ago

"How is this legal?" it isn't a girl. it's a 22-year-old guy in a gaming chair in Spain. he runs 4 instagram accounts as 4 different women. 612,000 combined followers. none of them exist. he merges two pinterest faces in Nano Banana Pro to create a girl who has never been born. records himself in his chair. plugs the video and her photo into Kling Motion Control. Kling transfers his movement onto her body frame-by-frame. sends the file to his phone via telegram. that step strips the AI-generated metadata. the platform sees regular user content. CapCut: grain 40, sharpness 20, brightness -8. plastic look gone. one of his accounts hit 156,000 followers in 21 days. same chair. same lighting. four women. one guy. details in the article. don't forget to save.

"How is this legal?" it isn't a girl. it's a 22-year-old guy in a gaming chair in Spain. he runs 4 instagram accounts as 4 different women. 612,000 combined followers. none of them exist. he merges two pinterest faces in Nano Banana Pro to create a girl who has never been born. records himself in his chair. plugs the video and her photo into Kling Motion Control. Kling transfers his movement onto her body frame-by-frame. sends the file to his phone via telegram. that step strips the AI-generated metadata. the platform sees regular user content. CapCut: grain 40, sharpness 20, brightness -8. plastic look gone. one of his accounts hit 156,000 followers in 21 days. same chair. same lighting. four women. one guy. details in the article. don't forget to save.

slash1s

218,777 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.

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.

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

THIS GUY TURNED HIMSELF INTO AN AI GIRL IN ONE SECOND. THE WORKFLOW BEHIND IT CAN RUN A $5,000/MONTH FANVUE PAGE IN 40 MINUTES A DAY 00:01 he raises his arms and instantly turns into an AI girl sitting in the same chair, ready to stream without ever showing his real face Claude creates the name, personality, captions and replies. one prompt can generate a full week of content while keeping the same character across TikTok and Fanvue ComfyUI with Flux builds the face and photo library. Kling 3.0 animates the images, then CapCut turns them into 30 to 50 short videos in one afternoon three daily streams and one clip reaching 400,000 views can send 70 buyers into a $15 Fanvue subscription. paid messages and tips are what push the page past $5,000 he stays behind the camera, Claude runs the brain and the AI girl becomes a business that works every day

THIS GUY TURNED HIMSELF INTO AN AI GIRL IN ONE SECOND. THE WORKFLOW BEHIND IT CAN RUN A $5,000/MONTH FANVUE PAGE IN 40 MINUTES A DAY 00:01 he raises his arms and instantly turns into an AI girl sitting in the same chair, ready to stream without ever showing his real face Claude creates the name, personality, captions and replies. one prompt can generate a full week of content while keeping the same character across TikTok and Fanvue ComfyUI with Flux builds the face and photo library. Kling 3.0 animates the images, then CapCut turns them into 30 to 50 short videos in one afternoon three daily streams and one clip reaching 400,000 views can send 70 buyers into a $15 Fanvue subscription. paid messages and tips are what push the page past $5,000 he stays behind the camera, Claude runs the brain and the AI girl becomes a business that works every day

Gipp 🦅

132,793 views • 3 months ago

Happy Horse 1.1 is finally here!! And HorsePower AI Cinema Awards Submissions Are Now Open! The Golden Acorn Heist was generated entirely with HappyHorse 1.1. A two-scene, 5-second heist sequence featuring Barnaby — a hyper-expressive flying squirrel - descending through a laser grid before triggering a plasma lava trap. The film pushes HappyHorse 1.5's core upgrades hard: orbital spiral camera moves with stable mid-air wire physics, violent whip-pan transitions, and a goggle-snap panic expression that lands in a single frame. Character detail stays locked across every cut via R2V multi-image referencing. Complex prompt. Zero drift. Whats New with Happy Horse 1.1? — Fluid motion under complex camera choreography (orbital spiral, whip-pan) — Consistent character detail across rapid scene cuts — Expressive facial rendering under high-motion conditions — Accurate response to multi-camera shot scheduling in a single prompt See what it can do >. HappyHorse #happyhorse #HorsepowerAICinema #happyhorse1.1 #ai

Happy Horse 1.1 is finally here!! And HorsePower AI Cinema Awards Submissions Are Now Open! The Golden Acorn Heist was generated entirely with HappyHorse 1.1. A two-scene, 5-second heist sequence featuring Barnaby — a hyper-expressive flying squirrel - descending through a laser grid before triggering a plasma lava trap. The film pushes HappyHorse 1.5's core upgrades hard: orbital spiral camera moves with stable mid-air wire physics, violent whip-pan transitions, and a goggle-snap panic expression that lands in a single frame. Character detail stays locked across every cut via R2V multi-image referencing. Complex prompt. Zero drift. Whats New with Happy Horse 1.1? — Fluid motion under complex camera choreography (orbital spiral, whip-pan) — Consistent character detail across rapid scene cuts — Expressive facial rendering under high-motion conditions — Accurate response to multi-camera shot scheduling in a single prompt See what it can do >. HappyHorse #happyhorse #HorsepowerAICinema #happyhorse1.1 #ai

ben.

21,377 views • 3 months ago

SHE GETS BLOCKED TWICE AND STILL COMES BACK UNTOUCHED This looks like a regular POV shot from a packed carnival booth. Stuffed bears are hanging above, people are moving through the crowd, and she is standing at the counter paying with her phone. Then the real stress test starts. A man carrying a child walks straight across the lens and completely hides her. A few seconds later, another woman with a kid and another man pass through the same frame and block her again. Both times, she disappears fully. And both times, she returns exactly the same: same spot, same posture, same payment moment, phone still in the same hand at the same angle. That is usually where AI video starts to fall apart. A clean shot with one subject is easy to keep stable. The hard part is when the subject gets covered, because the model has to remember what was behind the obstruction and bring it back without changing the face, hands, outfit, pose, timing or object placement. Here, nothing breaks. No face reset. No hand glitch. No outfit change. No phone jumping. No weird shift after she gets blocked. And the wildest part is that none of this was real. Not the booth. Not the stuffed bears. Not the random strangers passing through the frame at the perfect moment. That is why this clip stands out. AI is getting better at holding continuity through messy, crowded, real-world motion, not just clean cinematic scenes

SHE GETS BLOCKED TWICE AND STILL COMES BACK UNTOUCHED This looks like a regular POV shot from a packed carnival booth. Stuffed bears are hanging above, people are moving through the crowd, and she is standing at the counter paying with her phone. Then the real stress test starts. A man carrying a child walks straight across the lens and completely hides her. A few seconds later, another woman with a kid and another man pass through the same frame and block her again. Both times, she disappears fully. And both times, she returns exactly the same: same spot, same posture, same payment moment, phone still in the same hand at the same angle. That is usually where AI video starts to fall apart. A clean shot with one subject is easy to keep stable. The hard part is when the subject gets covered, because the model has to remember what was behind the obstruction and bring it back without changing the face, hands, outfit, pose, timing or object placement. Here, nothing breaks. No face reset. No hand glitch. No outfit change. No phone jumping. No weird shift after she gets blocked. And the wildest part is that none of this was real. Not the booth. Not the stuffed bears. Not the random strangers passing through the frame at the perfect moment. That is why this clip stands out. AI is getting better at holding continuity through messy, crowded, real-world motion, not just clean cinematic scenes

Inspector

542,128 views • 13 days ago

I don’t think AI “doesn’t work.” AI that feels fake doesn’t work. This app is getting ~90k downloads/month and doing $35k+/month mostly from viral AI clips. And from my experience, almost every “revenue estimate” tool for apps is wrong when it comes to organic traffic. Usually the real revenue is 1.5x–4x higher. I’ve already started using Arcads to build workflows for AI transformation videos for my weight loss app. The workflow is honestly simple: > Generate avatars with GPT Images 2.0 using JSON prompts > Download viral TikTok transformation videos > Use Kling Motion Control > Recreate the same style with a different influencer People underestimate how far AI UGC has already gone.

I don’t think AI “doesn’t work.” AI that feels fake doesn’t work. This app is getting ~90k downloads/month and doing $35k+/month mostly from viral AI clips. And from my experience, almost every “revenue estimate” tool for apps is wrong when it comes to organic traffic. Usually the real revenue is 1.5x–4x higher. I’ve already started using Arcads to build workflows for AI transformation videos for my weight loss app. The workflow is honestly simple: > Generate avatars with GPT Images 2.0 using JSON prompts > Download viral TikTok transformation videos > Use Kling Motion Control > Recreate the same style with a different influencer People underestimate how far AI UGC has already gone.

Alex Nguyen

91,683 views • 4 months ago

AI video is becoming less about generating a clip and more about controlling the entire scene. 🎬 MiniMax H3 is now available on Magnific, with a multimodal workflow that brings text, images, video and audio together. → Up to 9 image + 3 video + 3 audio references → 2K video up to 15 seconds → Native synced voice, music & sound effects → Motion transfer → Instruction-based video editing → Camera, character and voice control The interesting part is how much you can build from references. A moodboard, product, character, environment or motion reference can all become part of the same generation. And the sound is generated alongside the video, instead of being added afterward. MiniMax H3 on Magnific makes the whole process feel much closer to directing a scene than simply prompting a video.

AI video is becoming less about generating a clip and more about controlling the entire scene. 🎬 MiniMax H3 is now available on Magnific, with a multimodal workflow that brings text, images, video and audio together. → Up to 9 image + 3 video + 3 audio references → 2K video up to 15 seconds → Native synced voice, music & sound effects → Motion transfer → Instruction-based video editing → Camera, character and voice control The interesting part is how much you can build from references. A moodboard, product, character, environment or motion reference can all become part of the same generation. And the sound is generated alongside the video, instead of being added afterward. MiniMax H3 on Magnific makes the whole process feel much closer to directing a scene than simply prompting a video.

Mohini Shewale

63,995 views • 1 month ago