We present PoseDiff, a conditional diffusion model that unifies robot state estimation and control within a single framework. At its core, PoseDiff maps raw visual observations into structured robot states-such as 3D keypoints or joint angles-from a single RGB image, eliminating the need for multi-stage pipelines or auxiliary modalities. Building upon this foundation, PoseDiff extends naturally to video-to-action inverse dynamics: by conditioning on sparse video keyframes generated by world models, it produces smooth and continuous long-horizon action sequences through an overlap-averaging strategy. This unified design enables scalable and efficient integration of perception and control. On the DREAM dataset, PoseDiff achieves state-of-the-art accuracy and real-time performance for pose estimation. On Libero-Object manipulation tasks, it substantially improves success rates over existing inverse dynamics modules, even under strict offline settings. Together, these results show that PoseDiff provides a scalable, accurate, and efficient bridge between perception, planning, and control in embodied AI. The video visualization results can be found on the project page: https://haozhuo-zhang.github.io/PoseDiff-project-page/.
@article{arxiv.2509.24591,
title = {PoseDiff: A Unified Diffusion Model Bridging Robot Pose Estimation and Video-to-Action Control},
author = {Haozhuo Zhang and Michele Caprio and Jing Shao and Qiang Zhang and Jian Tang and Shanghang Zhang and Wei Pan},
journal= {arXiv preprint arXiv:2509.24591},
year = {2025}
}
Comments
The experimental setup and metrics lacks rigor, affecting the fairness of the comparisons