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。
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 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.
Naz & Eazy pulled up to the HOTEL LOBBY 🎤😭🔥 HOODBAGS had to jump on the
Naz & Eazy pulled up to the HOTEL LOBBY 🎤😭🔥 HOODBAGS had to jump on the viral Quavo & Takeoff Hotel Lobby AI trend… and somehow these two actually got bars. 🦝🐒 Who carried the track — NAZ or EAZY? 👀👇 Should we drop the FULL $HBAGS version? 😂🔥 🎲 Secure the Bags. #HotelLobby #HotelLobbyTrend #Quavo #Takeoff #AITrend #HOODBAGS #HBAGS #Memecoin #Crypto #Gaming #Shorts
£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.
AI INFLUENCER GENJUTSU Create the character. Choose the look. Then bring
AI INFLUENCER GENJUTSU Create the character. Choose the look. Then bring it to life. With the Higgsfield AI Influencer, creating an AI character is much more practical. Instead of building everything through complicated prompts, you can customize the character directly through https://t.co/zGHnQDkGt5
📹 China's New Humanoid Robot Shows Off Incredible Dance Moves China's UB
📹 China's New Humanoid Robot Shows Off Incredible Dance Moves China's UBTECH bionic humanoid robot has stunned audiences after performing ballet and waltz routines alongside human dancers during a live demonstration. Developed by UBTECH Robotics, the full-size robot features https://t.co/OT5LgSDby7
Here's the Tutorial • Open Mimic Motion in the Pollo AI app. • Upload an
Here's the Tutorial • Open Mimic Motion in the Pollo AI app. • Upload an image + a motion reference video. • Hit Create. • Save your result! Simple as that! https://t.co/DK4reLyYbh
SCARY 😱 or CREATIVE 🤩 Inspired by the legendary #gracejones I wanted to
SCARY 😱 or CREATIVE 🤩 Inspired by the legendary #gracejones I wanted to create a piece with a similar visual energy. The process was fairly simple. Once I had the visual of the face with the pink helmet, I fed it to Kling Motion Control with the trend driver video, I then layered in heavy glitch effects and dynamic visual texture to give it that raw edge. #aicreative #klingai̇ #aivideo
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
@itsPolloAI Here's how to use Mimic Motion 👇 1. Open the Pollo AI app an
@itsPolloAI Here's how to use Mimic Motion 👇 1. Open the Pollo AI app and choose Mimic Motion. 2. Upload your reference image and a motion reference video. 3. Tap Create. 4. Download and share your AI video! That's all, it only takes a few taps. https://t.co/Mem1UcGlm1
Traditional product showcase videos need venue setup, filming and editin
Traditional product showcase videos need venue setup, filming and editing.🙁 👏Now we integrated workflow + prompts. Replace product images to get a 360° product promo video instantly. Try it now👇 https://t.co/x6mAmicBLy https://t.co/3uC3RopZ1r
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.
Kling 3.0's motion control is ridiculously good You don't even need to d
Kling 3.0's motion control is ridiculously good You don't even need to describe anything, just: • upload a motion reference video • upload your character And it'll transfer the motion A few things I learned after testing: • use a clean, well-lit reference image (harsh shadows and heavy filters hurt the results) • make sure your character is fully visible from head to toe • the better the reference image, the more natural the animation looks Once you get those right, the quality is honestly impressive
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
🕷️ How I created the Spider Swing trend 👇 1️⃣ Create your character refe
🕷️ How I created the Spider Swing trend 👇 1️⃣ Create your character reference sheet 2️⃣ Upload your character sheet + Depth Previs reference video into Seedance 2.5 3️⃣ Generate the final video in @pixazoai That's it. One character. One motion reference. One cinematic superhero shot. 🕸️ 💬 Comment "Marvel" and I'll send you the prompt + assets. #aitrend #trendingnow #spiderman
AI just removed the biggest barrier in faceless content 👀 Watched this G
AI just removed the biggest barrier in faceless content 👀 Watched this Glorify demo and the Motion Control feature is actually insane: You upload any short motion reference video (guy in a cap waving hands) any character photo (the girl) = AI generates a full realistic video where the new character perfectly copies every movement, timing and micro-expression. No filming. No studio. No showing your face. No expensive editor. This is exactly how solopreneurs are quietly building $5k–$15k+/mo faceless channels in 2026: • AI business / motivation Shorts • Product ad creatives for ecom brands • Educational explainers • Story-driven Reels You can now create a week’s worth of high-quality video content in one afternoon instead of a full week of shooting and editing. The production cost just dropped to almost zero while output went up 10x. If you’re still thinking “I need to be on camera to make money with video” — this kind of tool just proved that wrong. What’s the first video you would create with Motion Control? Drop your niche below 👇
@higgsfield_ai Genjutsu is wild. 🤯 Genjutsu allows you to upload a motio
@higgsfield_ai Genjutsu is wild. 🤯 Genjutsu allows you to upload a motion reference and transfer the movement, acting, and overall performance to your own character. For this test, I used an existing video as my motion reference and applied the movement to my own avatar. The https://t.co/EBNIdfcRQ3
Here's exactly how to film yourself in a field and end up on a private b
Here's exactly how to film yourself in a field and end up on a private beach. step 1. shoot one clean video of yourself. a simple location is best, an empty field, a parking lot, a blank wall. good daylight, steady camera, your full body in frame. walk, talk, gesture naturally. this is your motion reference, so the movement matters more than the background. step 2. go to an AI video tool that supports video-to-video with a reference (OpenArt, Runway, Kling, Higgsfield all do this). upload your clip. step 3. tell it to keep your motion but rebuild the world. the prompt is the whole trick: "keep the exact body motion, timing and gestures from the reference video. replace the entire environment with a luxury tropical beach, white sand, turquoise water, a white modern villa and a yacht in the background, bright sunny daylight, cinematic." your movement stays, the field disappears. step 4. match the lighting or it looks fake. if you filmed in flat daylight, ask for "bright midday sun" so the shadows on you line up with the new scene. mismatched light is the number one thing that gives it away. step 5. generate, then fix. first result is usually 80 percent there. push back: "more realistic sand texture," "keep my face consistent," "remove the warping on my hands." 2 to 3 passes and it's clean. step 6. export, cut to 15 seconds, post. the swap between your real field and the fake beach IS the hook, so show both. the whole thing is one principle: film the performance once, generate the world as many times as you want. one take, infinite locations.
Alibaba rethinks character animation with Wan-Animate-2 Anyone who has t
Alibaba rethinks character animation with Wan-Animate-2 Anyone who has tried animating a character from a reference video knows the trade-off: you either lose identity, lose fine motion detail, or the compute cost becomes unbearable. Tongyi Lab, Alibaba's research team, just published a paper that tries to tackle all three problems at once, with a name that's already a statement: Wan-Animate-2. 🎬 The team frames the existing landscape around three paradigms and their flaws. Methods based on explicit motion representations suffer from extraction errors and identity drift, implicit motion features lose fine-grained dynamics through compression, and in-context learning avoids intermediate representations but incurs prohibitive computational costs. Wan-Animate-2 removes the intermediate motion extractor entirely, letting the driving video feed directly into a redesigned Diffusion Transformer. 📐 Architecturally, the framework conditions video generation on a reference image and a reference video through a dual-branch DiT, introducing Time-Align RoPE for temporal alignment between denoising video tokens and reference tokens, and Sparse-Ref Attention to selectively attend to the most informative reference features. 🕹️ One of the most interesting capabilities is text-driven viewpoint control, which decouples the output camera perspective from the driving video, a capability the authors describe as rarely supported by prior character animation methods that rely on explicit motion representations. ⚡ For interactive use cases, the team introduces Wan-Animate-2-Lite, an efficient variant designed to push inference latency down to real-time thresholds through a three-stage training paradigm combining teacher forcing pretraining with an error buffer mechanism and Self-Forcing distillation with chunk-wise backpropagation. According to independent reporting, this lite version reportedly reaches real-time streaming at 24 fps, opening deployment scenarios like digital avatars and live-streaming hosts that were previously infeasible at this quality level. Why it matters: if it holds up outside the demo reel, this changes the equation for anyone working with digital avatars, live streaming, or low-budget video production. No complex 3D rig, no expensive motion capture setup, just a reference video and an image. For companies and independent creators, it means access to professional-grade animation tooling without the hardware and pipeline barriers of traditional workflows, especially now that the team says it will release the Wan-Animate-2-Base model weights publicly. As a Creative & AI Specialist, what strikes me most isn't the visual quality itself, it's the decision to rethink the whole pipeline with live use cases in mind from day one: too many animation models stay stuck in offline mode because they were built for quality alone, while here the real-time variant looks like a design priority rather than an afterthought, and if the open weight release lives up to the paper, the real test will be how easily it slots into the workflows creators already rely on. 📱 Want AI updates on WhatsApp? DM me "AI" and I'll send you access. 🔔 Follow me for the last AI updates! #AInews #WanAnimate2 #Alibaba #CharacterAnimation #AIVideo
Testing product showcase using the all new @Hailuo_AI H3 model. Focused
Testing product showcase using the all new @Hailuo_AI H3 model. Focused on getting #AE like transitions and motion. I am really impressed with the details and prompt adherence. The output is native 2k which is great for product showcase. #MiniMaxH3 . https://t.co/7unZ5YF8fJ
This High-End Product Showcase Video Was Made Entirely With AI... Commen
This High-End Product Showcase Video Was Made Entirely With AI... Comment "Yes" for the prompt 👇 https://t.co/HsphdXfsH5
From 4 static product photos to a smooth 360° product video. No expensiv
From 4 static product photos to a smooth 360° product video. No expensive turntable. No full studio shoot. No complicated editing. FAV Studio helps ecommerce teams show every angle of a product in minutes. Shoes. Jewelry. Furniture. Fashion. The question is no longer “Can shoppers see the product?” It’s “Can they see enough to feel confident buying it?” Try it: https://t.co/4GbASTJ8xS
"Studio Product Showcase" prompt analysis 🎬 Clever trick: "one crisp hig
"Studio Product Showcase" prompt analysis 🎬 Clever trick: "one crisp highlight along the shell" makes a black product pop on pale grey. © Video & prompt: matissegarlic2205 on Higgsfield, used only to illustrate this review. 🙏 #PromptReview #AIVideo https://t.co/80LhFRJg0g
we live in a CRAZY ERA from now you can generate 100 videos a day in one
we live in a CRAZY ERA from now you can generate 100 videos a day in one click. with GENJUTSU on DAAS here's the exact prompt i used for this one: Create a photorealistic video using the uploaded image as the identity reference and the uploaded video as the motion reference. SUBJECT The subject is an adult woman aged 25 or older. Use the woman shown in the uploaded image as the visual identity reference. Preserve her facial features, face shape, eye color, nose shape, lip shape, skin tone, and natural hairstyle. HAIRSTYLE AND NAILS Her hair is straight and tied into a ponytail. Two blonde strands frame the front of her face. Her nails have a classic French manicure with a pale pink base and white tips. ACTION Reproduce the visible movements from the uploaded video in the same order and at the same pace. Match the head movements, hand gestures, facial expressions, body position, and direction of gaze. Do not add movements that are not present in the reference video. CAMERA Match the reference video's camera angle, framing, subject position, and duration. Keep the camera movement consistent with the original video. VISUAL CONSISTENCY Keep the same adult woman recognizable throughout the entire video. Maintain consistent facial features, hairstyle, clothing, body proportions, and nail design in every frame. IMAGE QUALITY Use realistic skin texture, natural lighting, accurate anatomy, anatomically correct hands, five fingers on each hand, realistic hair movement, and smooth transitions between frames. CLOTHING AND SCENE Keep the clothing and setting appropriate for a general audience. Preserve the original outfit when it is suitable for general audiences. Do not add revealing clothing, nudity, sexual content, or suggestive gestures. OUTPUT REQUIREMENTS Produce one continuous, photorealistic video. Follow the reference video's sequence of actions without adding unrelated scenes, extra people, text, logos, or watermarks. Prioritize identity consistency, accurate motion reproduction, and realistic visual quality.