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Example-based Motion Synthesis via Generative Motion Matching paper page: present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and... tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.show more

AK

502,779 subscribers

93,694 Aufrufe • vor 3 Jahren •via X (Twitter)

Wissenschaft & Technologie Bildung Kunst

Anya Rossi• Live Now

Private livecam show

8 Kommentare

Profilbild von Mark J. G

Mark J. Gvor 3 Jahren

Where is the blender plugin/code?

Profilbild von Jesse Wood

Jesse Woodvor 3 Jahren

GIFs are about to get a whole lot cooler 😎 "When the starting and ending frames are specified to be the same our method generates an infinite loop"

Profilbild von Michael Watson

Michael Watsonvor 3 Jahren

That is spectacular

Profilbild von Bobcat

Bobcatvor 3 Jahren

This is brilliant

Profilbild von Brayan Meza Castillo 👨‍💻

Brayan Meza Castillo 👨‍💻vor 3 Jahren

@ezdubs_bot English spanish

Profilbild von EzDubs

EzDubsvor 3 Jahren

@_akhaliq @BrayanMezaC_Dev Done! Here is your Spanish dub: 📺🔴 Dub 𝙔𝙤𝙪𝙏𝙪𝙗𝙚 videos: 💬🟢 Dub 𝙒𝙝𝙖𝙩𝙨𝘼𝙥𝙥 videos and voice memos:

Profilbild von EzDubs

EzDubsvor 3 Jahren

@BrayanMezaC_Dev Done! Here is your Spanish dub: 📺🔴 Dub 𝙔𝙤𝙪𝙏𝙪𝙗𝙚 videos: 💬🟢 Dub 𝙒𝙝𝙖𝙩𝙨𝘼𝙥𝙥 videos and voice memos:

Profilbild von Thread Reader App

Thread Reader Appvor 3 Jahren

@_akhaliq @MejoraConIA Sorry we only unroll consecutive tweets from the same author, but if you want to grab the whole convo try @pdfmakerapp! 🤖

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A symphony of complex motion. The motion of a complex exponential equation, visualized. [🎞️ thebrainmaze]

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AK

106,519 Aufrufe • vor 2 Jahren

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Seamless Human Motion Composition with Blended Positional Encodings Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.

AK

30,779 Aufrufe • vor 2 Jahren

Video2Humanoid. Still trouble with bad retargeted humanoid motions? Humanoids now are Easy to track any video using our new Neural Motion Retargeting (NMR) Method. Optimization-based motion retargeting methods like IK and GMR are solving a non-convex problem frame by frame, which makes them sensitive to initialization, hard to tune, and prone to poor local minima. The result is familiar: joint discontinuities, self-collisions, and unstable foot contact. Our solution is simple but effective : instead of optimizing each frame independently, learn the mapping between human motion and robot motion as a distribution via networks. 📊Results on Unitree G1 - 0 joint jumps - 54% fewer self-collision frames - 61% fewer joint limit violations - Faster convergence for downstream control policy training NMR turns motion retargeting from a fragile optimization problem into a learned, scalable pipeline for more stable humanoid motion. please visit for more visualization results. opensource:

Video2Humanoid. Still trouble with bad retargeted humanoid motions? Humanoids now are Easy to track any video using our new Neural Motion Retargeting (NMR) Method. Optimization-based motion retargeting methods like IK and GMR are solving a non-convex problem frame by frame, which makes them sensitive to initialization, hard to tune, and prone to poor local minima. The result is familiar: joint discontinuities, self-collisions, and unstable foot contact. Our solution is simple but effective : instead of optimizing each frame independently, learn the mapping between human motion and robot motion as a distribution via networks. 📊Results on Unitree G1 - 0 joint jumps - 54% fewer self-collision frames - 61% fewer joint limit violations - Faster convergence for downstream control policy training NMR turns motion retargeting from a fragile optimization problem into a learned, scalable pipeline for more stable humanoid motion. please visit for more visualization results. opensource:

Xiao-Xiao Long

24,048 Aufrufe • vor 1 Monat

What is missing to bring real-time motion research into AAA games and real-world robotics? We present MotionBricks, a step toward bridging this gap with two key components: - a single generative latent motion backbone covering 350,000+ motion skills, running at 15,000 FPS with 2 ms latency and substantially improved quality and reliability. - a unified smart primitive interface for locomotion, object / scene interaction, with fine-grained control over generated behaviors. Webpage: Code: Paper: (ACM TOG / SIGGRAPH 2026)

What is missing to bring real-time motion research into AAA games and real-world robotics? We present MotionBricks, a step toward bridging this gap with two key components: - a single generative latent motion backbone covering 350,000+ motion skills, running at 15,000 FPS with 2 ms latency and substantially improved quality and reliability. - a unified smart primitive interface for locomotion, object / scene interaction, with fine-grained control over generated behaviors. Webpage: Code: Paper: (ACM TOG / SIGGRAPH 2026)

tingwu.wang

151,418 Aufrufe • vor 1 Monat

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit! A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit! A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

Jason Peng

168,823 Aufrufe • vor 8 Monaten