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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 просмотров • 1 год назад •via X (Twitter)

Образование Наука и технологии Искусство

Anya Rossi• Live Now

Private livecam show

Комментарии: 11

Фото профиля 𝐙𝐞𝐧𝐠 💜

𝐙𝐞𝐧𝐠 💜1 год назад

Wow. This is next level!

Фото профиля OPEN

OPEN1 год назад

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

Фото профиля Brett Stuart

Brett Stuart1 год назад

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

Фото профиля K Slash

K Slash1 год назад

Can we use this today?

Фото профиля HaloVFX

HaloVFX1 год назад

Lets go!! Huge update

Фото профиля Madness Apex

Madness Apex1 год назад

🤯

Фото профиля Kabooki AI

Kabooki AI1 год назад

Looks great!

Фото профиля .

.1 год назад

How can I use it?

Фото профиля Pupologic

Pupologic1 год назад

Where and how can we use the Gen2?

Фото профиля Cr4zy3y3z

Cr4zy3y3z1 год назад

Blender integration 😍

Фото профиля neoCortez

neoCortez1 год назад

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

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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.

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Move AI

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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.

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CYANPUPPETS

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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:

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ComfyUI

28,385 просмотров • 2 месяцев назад

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Higgsfield AI 🧩

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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

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TPI

23,561 просмотров • 2 месяцев назад

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Ilir Aliu

101,793 просмотров • 1 год назад

$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 просмотров • 3 месяцев назад

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 просмотров • 1 год назад

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 просмотров • 2 месяцев назад

🎬 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 просмотров • 1 год назад

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Kingnet AI

31,023 просмотров • 1 год назад