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MUGEN Unifies Motion Understanding and Generation Efficiently

Zhankai Ye, Yukai Jin, Bingyang Wei, Bofan Li, Yusen Wu, Fangyi Li, Shangqian Gao, Xin Liu· July 31, 2026 View original

Key takeaways

  • MUGEN unifies motion understanding and generation with language efficiently.
  • It uses a single adaptive-length autoencoder and continuous latent representation.
  • The framework avoids the high computational costs and quality limits of prior methods.
  • MUGEN achieves state-of-the-art performance in motion generation and retrieval.

Who benefits

GamingRoboticsEntertainmentVirtual RealityAI Development

Summary

Researchers introduce MUGEN, a unified framework that efficiently handles both human motion understanding and generation using a single adaptive-length autoencoder and continuous latent representation. This approach avoids the quality limitations and high computational costs of previous discrete codebook or iterative diffusion methods.

This paper introduces MUGEN, a novel framework designed to unify the understanding and generation of human motion in conjunction with language. The goal is to advance physical AI systems that can seamlessly interpret, create, and communicate human behaviors. MUGEN addresses key limitations of prior unified motion-language systems, which often relied on discrete motion codebooks, leading to compromised generation quality or high computational overhead. MUGEN's core innovation is a single adaptive-length autoencoder that compresses any-length motion into a few continuous latent slots, serving as the system's sole motion representation. This design eliminates the need for discrete codebooks, iterative decoding stages, or complex diffusion heads, significantly reducing inference costs. The framework allows a language model to both generate these continuous latents for text-to-motion tasks and interpret them for motion understanding. By using depth-routed hidden states and a calibrated head, MUGEN achieves state-of-the-art performance on benchmarks like HumanML3D and SnapMoGen, outperforming existing methods in both generation quality and retrieval precision with significantly lower computational expense.

Why it matters

MUGEN offers a more efficient and higher-quality solution for creating AI systems that can understand and generate human motion from language, crucial for robotics, virtual reality, and character animation.

How to implement this in your domain

  1. 1Explore MUGEN's architecture for developing more natural and expressive virtual avatars or robotic movements.
  2. 2Integrate the framework into animation pipelines to automate character motion generation from text descriptions.
  3. 3Investigate its application in human-robot interaction for more intuitive communication through gestures.
  4. 4Evaluate MUGEN for creating realistic training simulations that require complex human motion.

Original post by Zhankai Ye, Yukai Jin, Bingyang Wei, Bofan Li, Yusen Wu, Fangyi Li, Shangqian Gao, Xin Liu

"arXiv:2607.27581v1 Announce Type: new Abstract: Grounding human motion in language, and language in motion, is a central step toward physical AI systems that can understand, generate, and communicate human behavior. Unified motion--language systems first coupled the two direction…"

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Originally posted by Zhankai Ye, Yukai Jin, Bingyang Wei, Bofan Li, Yusen Wu, Fangyi Li, Shangqian Gao, Xin Liu on X · view source

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