来自 X · Motion Transfer
动作迁移提示词
从 X 公开帖子精选的 Motion Control / Motion Transfer 视频提示词，可在 Oraclip 试用，并保留作者署名与原帖链接。
YOU'VE BEEN WRITING YOUR AI VIDEO PROMPTS WRONG THIS WHOLE TIME the most
YOU'VE BEEN WRITING YOUR AI VIDEO PROMPTS WRONG THIS WHOLE TIME the most expensive word in them is "cinematic" type "make it cinematic" and the model guesses. you watch, sigh, reroll. every reroll is billed, in credits, tokens or a whole evening directors never say "make it nice". they say "slow dolly in, rack focus to the hands, whip pan out". three names, one take the gap between an amateur prompt and a pro one isn't taste. it's vocabulary so here are 24 shot names, animated in the 3 videos below. save this for the next time you open claude, codex or any video model camera moves pan: camera turns left or right, stays in place tilt: turns up or down dolly in: the camera itself travels closer zoom in: only the lens gets closer, the background flattens tracking: moves alongside the subject orbit: circles the subject crane up: rises and reveals the whole scene dolly zoom: moves in while zooming out. the face holds, the world stretches lens and time rack focus: focus jumps from one subject to another handheld: small natural shake, feels real fpv: drone flight straight through the scene dutch angle: tilted horizon, something feels off slow motion: one second becomes four speed ramp: fast, then slow on the key moment, then fast timelapse: a whole day in a few seconds freeze frame: the action stops on one beat transitions whip pan: a blurred swipe into the next shot match cut: same shape, new scene smash cut: quiet to loud with no warning cross dissolve: one shot melts into the next dip to black: a breath between scenes wipe: a line pushes the new shot in zoom through: punch into an object, come out in the next scene j-cut: you hear the next scene before you see it the prompt i paste before any video job: "write every scene as: move + speed + subject + transition out. example: slow dolly in on the trader's hands, rack focus to the screen, whip pan out. use only these 24 names. if a scene needs something that isn't on the list, ask me before you render" vague prompts get paid for twice. named shots get paid for once
Last day until credit reset.. Felt like forever this month Kling 2.5 pro
Last day until credit reset.. Felt like forever this month Kling 2.5 prompt: Perfect visual consistency, unchanging style, Fluid extreme speed drone tracking shot. disorienting camera. blistering high-speed action, extreme fluid keyframes, smear frames exploding across every movement, whip-pan camera tracking, pure unbroken motion, every limb and cloth fold drawn with razor-sharp timing and weight. otter riding a dragon, clearly having fun. smooth, 2.5D hand drawn, consistent style, smear frames, foreshortening, at an angle to the viewer. Expensive Pixar-, Ghibli-, and Disney-level animation quality with amazing movement
#MercedesBenz #GClass #GWagon #W463 #EmeraldGreen #ModelCar #Diecast #Sc
#MercedesBenz #GClass #GWagon #W463 #EmeraldGreen #ModelCar #Diecast #ScaleModel #CarModel #Unboxing #ModelAssembly #DiecastCars #G63 #LuxuryCars #CarCollection #Miniatures #ToyCars #BuildProcess #Detailing #CarEnthusiast --- **Video Prompt (for AI video generation / recreation):** A satisfying high-detail time-lapse of building a 1:18 scale Mercedes-Benz G-Class (W463) Emerald Green Edition model car. Start with hands unboxing the premium green box on a wooden table, revealing the pre-painted green body, black chassis, seats, wheels, and detailed parts in a molded tray. Cut green plastic sprue pieces with flush cutters, gently brush them clean. Assemble the intricate black chassis with a precision screwdriver, install suspension and drivetrain. Place the detailed V8 engine under the opening hood. Show the luxurious black quilted leather interior with digital dashboard. Finish by opening the doors on the completed emerald-green G-Wagon, lightly dusting the glossy body with a soft brush, then display the finished model rotating slowly on a black turntable under soft studio lighting. Cinematic close-ups, smooth camera movements, ASMR assembly sounds, luxury product showcase style.
Seedance 2.0 Prompt: A detailed English prompt for AI video/image genera
Seedance 2.0 Prompt: A detailed English prompt for AI video/image generation based on this video: **Prompt:** A high-end, aesthetic commercial shot of **Bioderma Hydrabio Hyalu+ Serum**. * **Scene Details:** * A clear blue glass serum bottle with white text and a white dropper cap. * Extreme close-up shot of the glass pipette dispensing a clear, hydrating liquid drop onto smooth skin. * Smooth application of the serum glowing on skin, highlighting its hydrating texture. * Macro shot of air bubbles floating inside the transparent blue bottle liquid. * **Lighting & Style:** Studio lighting, clean aesthetic, ultra-sharp focus, cinematic macro commercial product showcase, pristine visual quality, 8k resolution.
Curiosity almost got the cat... but thankfully it was something eles. 👀
Curiosity almost got the cat... but thankfully it was something eles. 👀 Wait for that plot twist at the end! What did you think was in the box? 👇✨ Seedance 2.0 on @Flovaai Prompt: Style: Stylized 3D cartoon (think Luigi's Mansion meets Spider-Man: Into the Spider-Verse). Rich textures, dramatic cinematic lighting, and exaggerated, expressive character physics.Scene 1 (0.0 – 2.5s) | The DiscoveryVisual: A wide-eyed, fluffy cartoon cat freezes in a dark, dusty attic. Its glowing green eyes lock onto a massive, trembling cardboard box covered in "DO NOT OPEN" tape. Camera: Dynamic Push-in. Starts wide, then violently whips forward into an extreme close-up of the cat’s dilated pupils, reflecting the shaking box.Scene 2 (2.5 – 5.0s) | The ApproachVisual: The cat creeps forward on tip-toes. Suddenly, a massive, jagged shadow rises from behind the box, looming over the walls like a multi-legged monster. Camera: Low-Angle Tracking Shot. The camera skims along the floorboards right in front of the cat's paws, tilting up sharply to reveal the terrifying, growing shadow.Scene 3 (5.0 – 7.5s) | The SoundVisual: CREAK! The box lid violently pops open an inch. A puff of glowing green dust explodes out. The cat’s fur stands completely on end, sparks of static electricity flying. Camera: Orbit Arc Shot. The camera rapidly spins $360^\circ$ around the cat at high speed, capturing its sheer panic from every angle as the dust settles.Scene 4 (7.5 – 10.0s) | The Final StretchVisual: The cat decides to investigate. It reaches out a single, trembling paw toward the glowing gap in the box. A low, rumbling growl vibrates through the room. Camera: POV Crane Down. Looking directly from the inside of the dark box up at the cat’s approaching paw, plunging downward as the gap narrows.Scene 5 (10.0 – 12.5s) | The ClimaxVisual: The box bursts open completely! Glowing eyes flash from the darkness. The cat gasps, flying backward through the air in slow-motion cartoon terror. Camera: Zolly (Hitchcock Zoom). The background stretches dramatically while the cat stays fixed in the center of the frame, amplifying the sudden shock.Scene 6 (12.5 – 15.0s) | The TwistVisual: Time snaps back to normal speed. A tiny, adorable, glowing robotic toy mouse hops out of the box, happily squeaking and chasing its own tail. The cat faceplants into a pile of pillows in the background, looking utterly exhausted. Camera: Whip-Pan to Static. The camera rapidly whips away from the flying cat to lock onto the tiny mouse, ending on a crisp, comedic still shot.
🔥 This looks like a scene from an AI movie… But the movement came from a
🔥 This looks like a scene from an AI movie… But the movement came from a real reference video. Using @pixverse Depth Map Control, I combined: 🎬 Real motion reference 🌐 Depth guidance 🖼️ A new visual style The result? A character keeping the original action while stepping into a completely different world. From fight choreography to cinematic scene transformation, this workflow opens up new creative possibilities for AI video creators. More direction. More control. More ways to create. 👀 https://t.co/fVrLEdnw4j
Seedance 2.0 prompt Cyberpunk Neon Samurai Duel (High-energy action vira
Seedance 2.0 prompt Cyberpunk Neon Samurai Duel (High-energy action viral style)15-second ultra-cinematic cyberpunk VFX duel, anamorphic lens flares, 8K hyper-realistic, neon-noir rainy Shinjuku alley at night, torrential rain with realistic splashes and reflections on wet asphalt, pink/teal volumetric lighting, high-speed shutter with motion blur, realistic physics mixed with stylized slow-mo impacts, sparks and energy shockwaves, no text/logos, samurai maintains perfect face, glowing neural links, and katana consistency throughout, no deformation or extra limbs. 0–2s: Extreme low-angle tracking shot, cyber-samurai with glowing blue neural links strides powerfully through pouring rain, katana humming with electric energy. 2–4s: Wide whip-pan as holographic guards materialize, samurai launches into fluid sword flurry, blade collisions exploding bright sparks. 4–6s: Medium 120fps tracking shot, low sweeping kick sends one guard flying, camera follows the leg arc with realistic rain spray. 6–8s: Bullet-time 360° orbit, samurai deflects digital bullet rain with vibrating katana, creating expanding blue energy rings in slow motion. 8–10s: Vertical slow-mo jump spin, powerful downward strike cracks asphalt, concrete shards and rain droplets exploding upward in perfect spheres. 10–12s: Close-up whip-pan on samurai’s reflective visor, neon lights flickering as he spins to face the final guard. 12–14s: Extreme close-up on narrowed eyes, katana sheathes with sharp metallic click and electric discharge, background neon bokeh blur. 14–15s: Quick wide pull-back, samurai stands victorious amid settling rain and debris in a dramatic hero silhouette freeze.
Randi, allow me to demonstrate. 😂💃 I borrowed your video, dropped myself
Randi, allow me to demonstrate. 😂💃 I borrowed your video, dropped myself into it with Kling motion control and… yeah. This is why I keep recommending it. 😏 Same moves. Different girl. ❤️ Your turn, @RandiAthenas — give Kling another shot! 😂 #MikaMilani #KlingAI #MotionControl #AIVideo
A $40 AI CLIP AND A $40,000 SHOOT ARE SEPARATED BY TWO WORDS the two wor
A $40 AI CLIP AND A $40,000 SHOOT ARE SEPARATED BY TWO WORDS the two words are the camera move. most AI video prompts describe the subject and the light, leave the camera out, and get the same slow drift every time. the vocabulary a film crew uses, and what each move does to the viewer: > DOLLY ZOOM - the background stretches while the face stays put. the floor drops out > DOLLY IN - the camera moves toward the face. a realization lands > CRASH ZOOM - a snap zoom. shock, or the punchline > WHIP PAN - a pan so fast it blurs. energy, and it hides a cut > RACK FOCUS - focus jumps from the foreground to the subject. the reveal > ORBIT - the camera circles the subject. the hero moment > CRANE UP - rises and pulls back. endings, scale > TRACKING SHOT - moves alongside someone walking. momentum > FPV DIVE - a drone dive through the space. speed > LOW ANGLE - near the ground, looking up. power > DUTCH ANGLE - the horizon tilts. something is wrong > HANDHELD - a small shake. it feels filmed, not generated two habits matter more than the list. name the move before the subject, and give each shot one move. stack two in one prompt and you are asking the model to guess which one you meant. the $40,000 used to pay for the dolly track, the crane and the focus puller. now it pays for knowing their names.
A 21-YEAR-OLD GUY MAKES $10,000/MONTH WITHOUT FILMING OR ACTORS While br
A 21-YEAR-OLD GUY MAKES $10,000/MONTH WITHOUT FILMING OR ACTORS While brands spend thousands of dollars renting studios, finding models, and editing, this guy creates ready-made UGC advertising content in 3 minutes directly from his phone. No face in the frame, no expensive equipment, and no burnout. What's the gist of the process? He uses a combination of character generation and Motion Control (AI-powered motion transfer). Here's the whole process step by step: Photo model generation: Using Higgsfield AI, he creates or uploads an ultra-realistic model (indistinguishable from a real person, just from the outside). Motion recording: The guy simply grimaces in front of his phone camera—making the desired facial expressions, gestures, and delivery. The magic of Motion Control: He uploads his motion video to the Kling Motion Control module and links it to the generated AI model. Result: The neural network will precisely replicate every gesture and look on the face of a perfect AI model! How to make money from this? Selling UGC advertising to brands: Agencies and stores pay $100 to $500 for a single short video for TikTok Ads or Reels (or you know how else this can be used on other platforms). Creating AI influencers: Launching a virtual blogger who can advertise products 24/7. Traffic arbitrage: Uploading dozens of unique videos a day to offers without being banned for duplicate content. Bookmark this to replicate it and finally start earning real money.
Made with Seedance 2.0 and @higgsfield_ai 👀 🔥 Prompt: Cinematic AAA vide
Made with Seedance 2.0 and @higgsfield_ai 👀 🔥 Prompt: Cinematic AAA video game cutscene, epic fantasy action, dramatic volumetric god rays, heavy dust and debris, photorealistic with stylized game lighting, intense physics. 0-4s: Wide low-angle tracking shot following the hero sprinting through an ancient stone corridor. Sudden massive collapse behind him. Camera accelerates into a fast tracking shot as the floor cracks and drops away. 4-8s: Extreme dolly zoom (vertigo effect) on the hero’s face as he leaps across a widening chasm — background rushes toward camera while zooming in, creating strong disorientation. Hero twists mid-air grabbing a swinging chain. 8-12s: Dynamic orbiting camera spinning 180° around the hero while he swings across collapsing platforms. Camera tilts wildly with the falling debris, inducing vertigo. Hero runs across a tilting stone bridge that is breaking apart. 12-15s: Final desperate leap toward sunlight as the entire temple collapses into a massive sinkhole. Camera spins and drops with him in freefall, slow-motion rotation, ending with a dramatic pull-back as he lands on safe ground outside. Heavy motion blur on fast elements, coherent physics, intense energy. Photorealistic 8K, ultra-detailed stone textures, cinematic lighting, perfect motion blur, high dynamic range, stable character, movie-level stability, no artifacts.
How to Generate Videos with Ocean Inference I personally use Ocean Infer
How to Generate Videos with Ocean Inference I personally use Ocean Inference for my own projects. I have a YouTube channel, and since Inference became available, I've started using it to generate some of the scenes and shots for my videos. It's been a useful way to create https://t.co/tH1qe6lHJM
REAL influencers are losing to a dog 😭 meet casper. an AI italian greyho
REAL influencers are losing to a dog 😭 meet casper. an AI italian greyhound with 184K followers and 11.4M likes on tiktok he's been to the OLYMPICS. fought in a muay thai ring. deadlifted in the gym none of it ever happened here's why it WORKS: → one face you can NEVER forget → dropped into moments that already went viral → the footage is proven. only the character is new how to make your own: STEP 1: pick a character so weird people screenshot it STEP 2: find a clip that already has millions of views STEP 3: swap your character in with a motion transfer tool (higgsfield genjutsu, kling) STEP 4: same character. new viral moment. every day the hardest part is picking the dog
£142,485.84 FROM AN AI GIRL BUILT ON 5 TOOLS AND 1 NICHE. Here's the sta
£142,485.84 FROM AN AI GIRL BUILT ON 5 TOOLS AND 1 NICHE. Here's the stack. Claude writes every caption, every reply, and remembers each fan. Nano Banana Pro blends 2 real faces into a third that belongs to nobody. Kling Motion Control takes a viral clip and transfers the movement onto her, frame by frame. ComfyUI and Wan2.2 on RunPod handle the higher end renders. CapCut runs 1 pass that turns the render into a phone photo. Dark hair, a small scar above one brow, alt streetwear. She skips class on Tuesdays, posts a birthday dinner with fake friends, texts fans first when she's having a bad week. No camera. No studio. No second person on payroll. A pretty face gets 1 scroll. A girl with a niche gets followed for years. U just watched a business plan, not a girl.
Most AI videos fail at camera work, not visuals. I broke down a 15s AI s
Most AI videos fail at camera work, not visuals. I broke down a 15s AI short into the 4 camera moves that tell its story: a locked frame, a chase, a whip pan, and a dolly zoom. 🎬 The storyboard prompts for each shot are in the replies 👇 https://t.co/r811X2tC7s
A guy claims his “weird friend” makes $2,000 a week from a girl who does
A guy claims his “weird friend” makes $2,000 a week from a girl who doesn’t exist. And the workflow is almost stupidly simple. He creates one consistent AI model, picks a niche like fitness or cosplay, then finds viral TikToks with minimal movement and downloads them. He screenshots the first frame, uses a custom GPT to turn it into a detailed JSON prompt, recreates the scene with Nano Banana, then feeds the image and original video into Kling Motion Control. The result is basically the same viral video, but with his AI character replacing the real creator. He posts 2–3 clips a day, pushes the traffic to Fanvue, then sells subscriptions and pay-per-view content. This isn’t some magical passive-income hack. It’s content arbitrage with a synthetic face. And apparently, that’s already enough to become the “weird friend” everyone asks about.
The only way to grow an UGC account which is AI is to just copy from rea
The only way to grow an UGC account which is AI is to just copy from real videos. If you try to make videos from scratch you are just trying to waste your money. The way in which this works is not just using doing motion control. There are two steps to finding it. -> First, you need to know for what kind of videos what models are working. For example, Kling motion control works when the camera is not moving. But when the camera is moving too much, Kling motion control doesn't work. -> So what you do is that you take a copy of the first frame of the original video, you draft a prompt from the original video and you try to use something like that, like what I've done in the video below. These are the nuances one needs to understand to run hundreds of accounts at scale, you need a process you cant write prompts and sit there and make videos one by one Ghostfeed mcp has created 70 new videos in last 24 hours for this new Avatar
THE PROMPT ISN’T THE MOST VALUABLE PART OF A VIRAL AI VIDEO ANYMORE the
THE PROMPT ISN’T THE MOST VALUABLE PART OF A VIRAL AI VIDEO ANYMORE the motion is. look at this clip: girl runs across a rooftop → jumps several floors → hits a trampoline → backflips straight back onto the roof you understand the entire setup in the first second and now your brain has to see the landing normally you’d try to recreate something like this with a giant text prompt and pray the model understands the camera, timing and body movement but there’s a much smarter way now use the viral clip itself as the motion reference Wan2.2 Animate can take a driving video like this and transfer the movement + expressions onto a completely different character or use its character replacement mode and swap the person while keeping the original movement, scene and camera behavior there’s already a native ComfyUI workflow for it, and people have open sourced full "trend copy" pipelines around the same idea so the stack becomes: 1. find a format that already holds attention 2. use the original as driving motion 3. replace the subject / character 4. regenerate the creative around your own idea no keyframing the stunt no manually rebuilding the camera move no hoping a text prompt invents the same physics this is the part of AI video I think people are sleeping on viral videos are slowly becoming motion templates. the footage is disposable. the movement is reusable. workflows below 👇
How I Created a Complete Anime Scene from 3D Model Motion 🎬⚔️🐉 This time
How I Created a Complete Anime Scene from 3D Model Motion 🎬⚔️🐉 This time, I ran the experiment with GPT‑6 Astra. I wanted to find out whether a basic 3D animation could be transformed into a cinematic anime scene while preserving the original motion. The goal was not merely to create a video that looked similar. I wanted the anime warrior to perform the same actions at the same pace and with the same camera movement. The final video places the anime version at the top and the motion source at the bottom, allowing viewers to compare them in real time. I worked inside ChatGPT Work and combined several tools, each responsible for a different stage of the process. Stage One: The Original 3D Animation I started with an eight second video featuring two grey 3D models. A sword fighter battles a large monster. He dodges its attack, strikes back and eventually brings the creature down. This video served as the motion source for the scene. It defined the body poses, sword movements, distance between the characters, pacing and camera movement. This is an important part of the process. When you want a specific movement, simply writing “the character fights” in a prompt is not enough. It is better to provide a video that shows the system exactly what the movement should look like. Stage Two: Establishing the Visual Language Before generating the anime, I defined how the film should look. I created a reference image featuring a consistent samurai character: Black hair tied back A dark blue Japanese outfit Black shoulder armor A red sash A katana I also established the design of the monster and the location. The scene takes place in a Japanese village at dusk, with cool blue lighting, warm lanterns, light mist and mountains in the background. The reference image became the visual identity sheet for the film. I returned to it throughout the process to preserve the same warrior, clothing and atmosphere. Stage Three: Turning the First Fight into Anime I uploaded the original 3D animation to Genjutsu as the driving video. Alongside it, I added the samurai and monster image as a visual reference. The 3D animation determined what the characters would do. The reference image determined how they should look. This created the first part of the film, in which the anime warrior performs the same combat actions as the grey 3D model. It is important to understand that motion transfer does not always produce a perfect match for every finger and joint. The system may still change small details, but it preserves the overall structure of the action and the camera movement. Stage Four: Building a 3D Continuation Next, I wanted to continue the story. After completing the fight, the warrior walks through the village. A dragon suddenly appears. They fight, and the warrior eventually manages to climb onto the dragon and fly with it toward the horizon. I built a new 3D scene in Higgsfield 3D Jutsu that included: A white sword fighter A white dragon with four legs and two wings A Japanese village Houses on both sides of the road A camera following the action I created an animation lasting approximately twenty seconds and rendered it as a white model video. After rendering, it became clear that the motion was not good enough. The body movements were stiff, climbing onto the dragon did not feel natural and the wing movements looked mechanical. When I transferred this footage into anime, the stiffness was transferred along with it. That was an important lesson: an attractive visual design cannot repair a weak motion foundation. I therefore rejected this version of the continuation and did not use it in the final film. Stage Five: Rebuilding the Continuation with Seedance 2.5 Instead of starting another manual animation, I used the original 3D video as the foundation for the continuation. I uploaded it to Seedance 2.5 in forward video extension mode and asked the model to begin exactly from the final frame, where the monster is lying on the ground and the warrior is standing behind it. I created a 24 second continuation and divided the action into clear time windows: From 0 to 5 seconds, the warrior recovers and walks toward the village From 5 to 8 seconds, the dragon arrives and lands in front of him From 8 to 15 seconds, the fight takes place From 15 to 19 seconds, the warrior grabs the dragon, climbs onto it and sits down From 19 to 24 seconds, the dragon pushes off the ground, spreads its wings and takes flight In the prompt, I emphasized that the system should preserve the white and grey models while creating natural human movement, planted feet, visible body weight and continuous action without sudden jumps or the warrior magically appearing on the dragon. The result was a new white motion reference that looked like a continuation of the original 3D animation. This section is a white reference video generated with an AI video model. It is not a 3D file that can be opened and edited in Blender. Stage Six: Checking the Motion Before creating the anime version, I reviewed the new motion video. I focused on four key questions: Does the walking look natural? Is there clear physical contact during the fight? Does the warrior actually climb onto the dragon rather than suddenly appearing on it? Does he remain seated during takeoff? Only after these actions appeared clearly did I move on to generating the anime version. Stage Seven: Transferring the Continuation into Anime I uploaded the white motion video to Genjutsu as the driving video. I added three visual references: The final frame of the first anime fight The samurai character reference The dragon design reference I asked the system to preserve the entire motion sequence, including the footsteps, sword movements, contact with the dragon, climbing action, seated position, wingbeats and takeoff. The white character became the established samurai. The white dragon became a blue grey dragon with pale horns and copper colored wings. The white environment became a lantern lit Japanese village beneath an evening sky. Stage Eight: Editing the Complete Film I edited the final video in Higgsedit. In the first section, I placed the anime footage at the top and the original 3D animation at the bottom. In the second section, I placed the anime continuation at the top and the white motion reference generated with Seedance 2.5 at the bottom. Both layers were set to 24 frames per second and began at exactly the same time. I did not change the speed of the anime or shift it in relation to the motion source. I connected the eight second opening fight to the 24 second continuation, creating a complete film lasting approximately 32 seconds. Stage Nine: Sound Effects and Music Finally, I added a sound layer featuring footsteps, sword movements, impacts, dragon breathing, wingbeats and the sound of takeoff. I placed cinematic music underneath the effects at a lower volume so that it supported the action without overpowering it. I then balanced all the audio layers and exported two versions: A full screen anime film A comparison video with the anime at the top and the motion source at the bottom What Did I Learn from the Process? A video like this is not created with a single click. It requires a combination of character design, visual development, motion creation, continuity testing, style transfer, editing and sound design. The 3D animation provided the motion foundation. Seedance 2.5 created the continuation. Genjutsu transformed the movement into anime. Higgsedit combined all the elements into one complete film. The most important lesson is that the quality of the anime depends first and foremost on the quality of the underlying motion. If the motion source is stiff, the anime will also look stiff. When the movement is constructed properly, AI can be used to replace the characters, style and location while preserving the same cinematic action. The transfer is still not perfectly identical in every frame, but this workflow provides far more control than generating an anime video from a text prompt alone.
1/ Let’s kick things off with a quick experiment. 🧪 Try Depth Map Contro
1/ Let’s kick things off with a quick experiment. 🧪 Try Depth Map Control with @pixverse: https://t.co/uLxir3LFML Grab a motion reference, inject your own creative concept, and watch how the AI completely transforms it. The reference video acts as your blueprint for: → movement → camera flow → action rhythm That original human motion serves as your unbreakable foundation.
Higgsfield launched Genjutsu Aug 31, an AI tool that preserves a clip's
Higgsfield launched Genjutsu Aug 31, an AI tool that preserves a clip's motion and camera work while swapping people, places, or props. Two modes: Motion Transfer for full scenes, Object Swap for single elements. 30+ presets require no prompt. https://t.co/XTMSLwsdEx
Dancing with yourself on a rooftop 💃 but suddenly your anime twin joins
Dancing with yourself on a rooftop 💃 but suddenly your anime twin joins in 😭🔥 Would you do this challenge? 👇 GPT Image 2 and Seedance 2.0 on @VorlaAI JOIN US 👉https://t.co/AiBl04Xu9v #ai #AIvideo #AIart #CinematicAI #GenerativeAI #dance #anime https://t.co/QlR0I5VZxt
Turned single product shot into full product showcase video in 1 minute
Turned single product shot into full product showcase video in 1 minute Prompt in comments below👇 https://t.co/k8KJejWJeg
STYLE: High-end stylized 2.5D animation with deep 3D parallax layers, ci
STYLE: High-end stylized 2.5D animation with deep 3D parallax layers, cinematic 2.5D splash art aesthetics, xianxia visual style, traditional Chinese ink wash splatter effects, floating gold dust particles, smoke effects, ethereal volumetric lighting. Maintain the flat 2D https://t.co/OiXfF2rQ0E