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Introducing Gen 2 spatial motion: 3D motion capture, full-body dynamics, joint torques, ground reaction forces, advanced motion retargeting, motion prediction. Works on AI video, phones, cinema cams, stadia. Launched at Movement Day, BAFTA London.

Move AI

22,269 subscribers

68,729 次观看 • 1 年前 •via X (Twitter)

教育科学技术艺术

Anya Rossi• Live Now

Private livecam show

11 条评论

𝐙𝐞𝐧𝐠 💜 的头像

𝐙𝐞𝐧𝐠 💜1 年前

Wow. This is next level!

OPEN 的头像

OPEN1 年前

Cinematic pedigree of the highest order meets innovative AAA gameplay in OP3N. Dive into the action by wishlisting on Epic Games TODAY!

Brett Stuart 的头像

Brett Stuart1 年前

Wonder Dynamics gunna get run for their money. Looking great!

K Slash 的头像

K Slash1 年前

Can we use this today?

HaloVFX 的头像

HaloVFX1 年前

Lets go!! Huge update

Madness Apex 的头像

Madness Apex1 年前

🤯

Kabooki AI 的头像

Kabooki AI1 年前

Looks great!

. 的头像

.1 年前

How can I use it?

Pupologic 的头像

Pupologic1 年前

Where and how can we use the Gen2?

Cr4zy3y3z 的头像

Cr4zy3y3z1 年前

Blender integration 😍

neoCortez 的头像

neoCortez1 年前

This is awesome. Can’t wait to test it out

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PICO Motion Tracker and VRChat have joined forces! On the launch day of PICO Motion Tracker, get ready to immerse yourself in the VRChat universe with full-body motion capture. Unleash your creativity, express yourself like never before. #PICOMotionTracker #VRChat #VRRevolution

PICO Motion Tracker and VRChat have joined forces! On the launch day of PICO Motion Tracker, get ready to immerse yourself in the VRChat universe with full-body motion capture. Unleash your creativity, express yourself like never before. #PICOMotionTracker #VRChat #VRRevolution

PICO XR

377,394 次观看 • 2 年前

Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

Reborn

95,507 次观看 • 1 年前

Unitree Humanoid Robots Updated With Open-Source Full-Body Motion Dataset 🤗 We’ve adapted motion capture data for Unitree H1, H1-2, and G1 humanoid robots, using numerical optimization methods to make their movement trajectories more natural. Open-Source： #AI #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI #Dance

Unitree Humanoid Robots Updated With Open-Source Full-Body Motion Dataset 🤗 We’ve adapted motion capture data for Unitree H1, H1-2, and G1 humanoid robots, using numerical optimization methods to make their movement trajectories more natural. Open-Source： #AI #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI #Dance

Unitree

198,583 次观看 • 1 年前

Pure Precision. Move AI is pushing 3D character animation and motion capture forward with these powerful releases: – Dex by Move AI: significantly improved hand & finger tracking – New desktop apps: Move Pro now available on Windows, macOS, and Linux – Editor (Beta): fine-tune motion input data with precision Built for animators, developers, and researchers who demand lifelike motion — with total control, without suits. Watch the full video here →

Pure Precision. Move AI is pushing 3D character animation and motion capture forward with these powerful releases: – Dex by Move AI: significantly improved hand & finger tracking – New desktop apps: Move Pro now available on Windows, macOS, and Linux – Editor (Beta): fine-tune motion input data with precision Built for animators, developers, and researchers who demand lifelike motion — with total control, without suits. Watch the full video here →

Move AI

33,144 次观看 • 1 年前

We present EgoReAct: Real-time 3D human reaction generation from streaming egocentric video. 🌟Reacting to streaming egocentric video is something humans do every day. We hope EgoReAct makes human motion more human-like. 🔎 What we found: existing ego-reaction data can be spatially inconsistent (e.g., moving reactions paired with fixed-camera videos), which breaks 3D grounding. 📷 What we built: HRD, a spatially aligned egocentric video–reaction dataset (3,500 pairs, 32 categories), plus a spatially aligned ViMo fix for fair evaluation. (Instead of collecting expensive ground-truth motion, we employ VDM to generate the egocentric videos.) 👁️⚡🏃 Our simple yet effective pipeline: motion tokenization for compact discrete codes + an autoregressive Transformer for online, strictly-causal generation. Metric depth and head dynamics further improve 3D spatial consistency. Project Page: ArXiv: #HumanMotion #EgocentricVision #3D #ARVR #Animation #AIGC #DeepLearning #GenerativeAI #Graphics #ComputerVision #Motion

We present EgoReAct: Real-time 3D human reaction generation from streaming egocentric video. 🌟Reacting to streaming egocentric video is something humans do every day. We hope EgoReAct makes human motion more human-like. 🔎 What we found: existing ego-reaction data can be spatially inconsistent (e.g., moving reactions paired with fixed-camera videos), which breaks 3D grounding. 📷 What we built: HRD, a spatially aligned egocentric video–reaction dataset (3,500 pairs, 32 categories), plus a spatially aligned ViMo fix for fair evaluation. (Instead of collecting expensive ground-truth motion, we employ VDM to generate the egocentric videos.) 👁️⚡🏃 Our simple yet effective pipeline: motion tokenization for compact discrete codes + an autoregressive Transformer for online, strictly-causal generation. Metric depth and head dynamics further improve 3D spatial consistency. Project Page: ArXiv: #HumanMotion #EgocentricVision #3D #ARVR #Animation #AIGC #DeepLearning #GenerativeAI #Graphics #ComputerVision #Motion

Zhiyang (Frank) Dou

11,392 次观看 • 8 个月前

Free Blender AI motion capture plugin — also compatible with UE and Unity. Connects to a 1080P webcam and runs locally with a 1-billion-parameter model, requiring 8GB of GPU memory for real-time processing. Supports single-camera real-time capture and video uploads with no limit on the number of uploads. Includes full-body, facial, and finger motion capture. Completely free to use.

Free Blender AI motion capture plugin — also compatible with UE and Unity. Connects to a 1080P webcam and runs locally with a 1-billion-parameter model, requiring 8GB of GPU memory for real-time processing. Supports single-camera real-time capture and video uploads with no limit on the number of uploads. Includes full-body, facial, and finger motion capture. Completely free to use.

CYANPUPPETS

57,643 次观看 • 1 年前

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

Boyi Li

67,557 次观看 • 8 个月前

Most AI video work is just "generate and hope." This is different. Creator seungho__yeo ( IG ) rebuilt a live drum performance from the ground up. Using the drummer's actual motion data to redesign background mood, lighting, and spatial depth frame by frame. The pipeline: → Motion Tracking → Depth Mapping → AI Edit ( Seedance 2) → AI Relighting → Background Reconstruction → Cinematic Color Grading All built inside ComfyUI. This is what intentional AI production looks like. Not more generations, better direction. This is the level ComfyUI creators are working at.

Most AI video work is just "generate and hope." This is different. Creator seungho__yeo ( IG ) rebuilt a live drum performance from the ground up. Using the drummer's actual motion data to redesign background mood, lighting, and spatial depth frame by frame. The pipeline: → Motion Tracking → Depth Mapping → AI Edit ( Seedance 2) → AI Relighting → Background Reconstruction → Cinematic Color Grading All built inside ComfyUI. This is what intentional AI production looks like. Not more generations, better direction. This is the level ComfyUI creators are working at.

ComfyUI

28,385 次观看 • 2 个月前

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,835 次观看 • 8 个月前

KLING 2.6 MOTION CONTROL is LIVE on Higgsfield. TRY FREE. DAY-0 access. As always. Fast & complex movements finally work in AI video. Full-body sync, expression mapping, 30-second generations. Everyone else is behind. For 9 hours: retweet & reply & like & follow for 249 credits

KLING 2.6 MOTION CONTROL is LIVE on Higgsfield. TRY FREE. DAY-0 access. As always. Fast & complex movements finally work in AI video. Full-body sync, expression mapping, 30-second generations. Everyone else is behind. For 9 hours: retweet & reply & like & follow for 249 credits

Higgsfield AI 🧩

1,581,588 次观看 • 8 个月前

Many amateurs wait until the top of the backswing to initiate the downswing. Elite players do not. When we look at force plate data from the best ball strikers in the world, the majority begin pushing against the ground well before the backswing is complete. These ground reaction forces help decelerate the pelvis so the player can lead with the lower body on the downswing. Force precedes motion. New video on our YouTube channel. Link below

Many amateurs wait until the top of the backswing to initiate the downswing. Elite players do not. When we look at force plate data from the best ball strikers in the world, the majority begin pushing against the ground well before the backswing is complete. These ground reaction forces help decelerate the pelvis so the player can lead with the lower body on the downswing. Force precedes motion. New video on our YouTube channel. Link below

TPI

23,561 次观看 • 2 个月前

Check out this drone: Joshua Bird built this drone ($20) in his dorm! [open-source motion capture system⬇️] Built at low cost, a motion capture system for tracking & and flying drones autonomously, with millimeter-level precision at room-scale. The student used $1 PS3 Eye cameras with 150fps capability. The challenge? PID tuning! It took him 4 days of crashes to get the drone to hover, but it's still wobbly. Used a 3x nested PID loop for precise control. This project led to his dissertation on visual SLAM! Full details - Algorithms for camera positioning & obstacle triangulation, in his YouTube video: Code & 3D files on GitHub: More projects at

Check out this drone: Joshua Bird built this drone ($20) in his dorm! [open-source motion capture system⬇️] Built at low cost, a motion capture system for tracking & and flying drones autonomously, with millimeter-level precision at room-scale. The student used $1 PS3 Eye cameras with 150fps capability. The challenge? PID tuning! It took him 4 days of crashes to get the drone to hover, but it's still wobbly. Used a 3x nested PID loop for precise control. This project led to his dissertation on visual SLAM! Full details - Algorithms for camera positioning & obstacle triangulation, in his YouTube video: Code & 3D files on GitHub: More projects at

Ilir Aliu

101,793 次观看 • 1 年前

$P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: We’re launching with 70% discounted usage for the coming two days with our launch partners. each::labs, Replicate, Runware, Scenario, WaveSpeedAI, Wiro, Tellers, Soon also on Together AI We’d love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐$

P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: We’re launching with 70% discounted usage for the coming two days with our launch partners. each::labs, Replicate, Runware, Scenario, WaveSpeedAI, Wiro, Tellers, Soon also on Together AI We’d love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐

Pruna AI

22,727 次观看 • 3 个月前

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

AK

149,079 次观看 • 1 年前

On Figma Motion, my demo & review. This is one of those updates that clicked swiftly into my workflow. Like in my 3D pipeline, using this stick shift animation, I emphasize hardware feedback with motion from Figma Motion. Assets are animated in Fig Motion and loaded into Blender as video textures for mesh and material control. It's advantageous since it is built into an ubiquitous ecosystem. A lot of work these days start in Figma even for someone like me that works outside the tool frequently. Can't over-emphasize my appreciation for the schema used to introduce motion to nearly all properties. Reminds me of how motion works in Blender - with better complexity management. Better than introducing more menus for those. Ease curves iD between keyframes is also an improvement over existing systems. Having spent some time on the tool, here are updates I'll like to see to Figma Motion. These are based on my immediate needs as at the time of working on this demo: • Using modifier keys (CMD, Shift, Ctrl) to jump / nudge across the timeline. E.g: holding down Shift key to make the playhead jump 100ms etc. • Jump to previous or next keyframe. • Better anchor point trails • Renaming layers from the timeline as well. • Editable custom bezier styles: after saving a style, I should be able to edit the curve - just as you can edit colour styles. • Enable transform on the timeline. I should be able to flip or scale keyframes. I hope to continue trying more ideas. Craft focused tools have had to take a backseat in recent times. Pleasing to see this come to light.

On Figma Motion, my demo & review. This is one of those updates that clicked swiftly into my workflow. Like in my 3D pipeline, using this stick shift animation, I emphasize hardware feedback with motion from Figma Motion. Assets are animated in Fig Motion and loaded into Blender as video textures for mesh and material control. It's advantageous since it is built into an ubiquitous ecosystem. A lot of work these days start in Figma even for someone like me that works outside the tool frequently. Can't over-emphasize my appreciation for the schema used to introduce motion to nearly all properties. Reminds me of how motion works in Blender - with better complexity management. Better than introducing more menus for those. Ease curves iD between keyframes is also an improvement over existing systems. Having spent some time on the tool, here are updates I'll like to see to Figma Motion. These are based on my immediate needs as at the time of working on this demo: • Using modifier keys (CMD, Shift, Ctrl) to jump / nudge across the timeline. E.g: holding down Shift key to make the playhead jump 100ms etc. • Jump to previous or next keyframe. • Better anchor point trails • Renaming layers from the timeline as well. • Editable custom bezier styles: after saving a style, I should be able to edit the curve - just as you can edit colour styles. • Enable transform on the timeline. I should be able to flip or scale keyframes. I hope to continue trying more ideas. Craft focused tools have had to take a backseat in recent times. Pleasing to see this come to light.

seyi

29,245 次观看 • 2 个月前

🎬 Introducing MuseSteamer—an image-to-video model newly launched at Baidu AI Day. Here's what it offers: - Generation of synchronized visuals, Chinese dialogue, and sound effects from a single image. - Creation of 10-second, 1080P cinematic-quality clips with nuanced facial expressions and camera motion. - A model family that includes Turbo (available in beta now), Lite, Pro, and a full lineup of voiced versions. MuseSteamer is now integrated into Baidu Search. It's built to serve both everyday creators and professional film studios. Explore MuseSteamer:

🎬 Introducing MuseSteamer—an image-to-video model newly launched at Baidu AI Day. Here's what it offers: - Generation of synchronized visuals, Chinese dialogue, and sound effects from a single image. - Creation of 10-second, 1080P cinematic-quality clips with nuanced facial expressions and camera motion. - A model family that includes Turbo (available in beta now), Lite, Pro, and a full lineup of voiced versions. MuseSteamer is now integrated into Baidu Search. It's built to serve both everyday creators and professional film studios. Explore MuseSteamer:

Baidu Inc.

43,731 次观看 • 1 年前

🌐 New website is now live! First up is the light mode, with dark mode coming soon. We're thrilled about what's next— ready to scream for these upcoming updates? 🌀 Brand New Roadmap (some product timelines may be adjusted based on development progress) 🌀 Kingnet AI V2 Official Release – A completely revamped dApp experience! Next week, we’ll officially open access to the Animation Generator and Agent, making interactions smarter and simpler than the first-gen version 🌀 Public Docs & GitHub Code Open-Source 🌀 Official Deployment on Solana & BNB Chain – Users will be able to pay on-chain to generate custom assets and export them (FBX format for direct download and use). TON integration will follow. 🌀$KNET’s First Utility Feature As always, we’ve included a surprise at the end of our video, showcasing the full workflow of AI text-to-3D model to in-game motion model: 1️⃣ Enter the Agent virtual assistant, use text prompts to generate a 3D character motion model. 2️⃣ The Agent generates a 3D base model, automatically bakes and saves textures. 3️⃣ Auto-rigging, joint adjustments, and natural motion generation. 4️⃣ Users can pay to download the FBX file with animations. 5️⃣ The file can be directly imported into Unity or Unreal Engine, with options to import animations, set animation cuts & loops—ready for in-game use! Continuous Delivery.

Kingnet AI

31,023 次观看 • 1 年前