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arXiv · 2610.32966

ViT-H: Generative Video-To-Humanoid Control with Motion Tracking Feedback

Zimo He·Tianyi Bian·Zizhong Yang·Hongjie Li·Nan Jiang·Jieming Cui·Baoxiong Jia·Yizhou Wang·Siyuan Huang·Yixin Zhu
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Abstract

Learning humanoid control from monocular human videos offers a scalable way to acquire diverse whole-body behaviors. Yet, existing reconstruction-retargeting-tracking pipelines are limited in system-level scalability by separately optimized intermediate stages and cannot directly optimize reference motion executability using downstream tracking feedback. We present ViT-H, a video-to-humanoid control framework following a generation-and-tracking paradigm. ViT-H directly generates humanoid reference motions from videos with a diffusion model, bypassing human motion reconstruction and retargeting, and uses motion-tracking outcomes to post-train the generator with direct preference optimization. We further introduce an autoregressive variant that enables real-time humanoid video teleoperation with sub-400 ms latency. Extensive experiments on the Unitree G1 demonstrate that ViT-H outperforms state-of-the-art reconstruction-retargeting-tracking pipelines in both motion quality and executability. We further show that ViT-H scales favorably with model size and with both pre-training and post-training data scale, while motion-tracking feedback consistently improves executability across different motion trackers.

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Zimo He, Tianyi Bian, Zizhong Yang, Hongjie Li, Nan Jiang, Jieming Cui, Baoxiong Jia, Yizhou Wang, Siyuan Huang, Yixin Zhu. 2026-10-23. ViT-H: Generative Video-To-Humanoid Control with Motion Tracking Feedback. https://arxiv.org/abs/2610.32966

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