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

The first four authors contributed equally. Project website: https://awfuact.github.io/vit-h/

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.

兴趣：humanoid robots and whole-body control