WATCH: World-aware Allied Trajectory and pose reconstruction for Camera and Human

• URL: http://arxiv.org/abs/2509.04600v1
• Date: Thu, 04 Sep 2025 18:29:48 GMT
• Title: WATCH: World-aware Allied Trajectory and pose reconstruction for Camera and Human
• Authors: Qijun Ying, Zhongyuan Hu, Rui Zhang, Ronghui Li, Yu Lu, Zijiao Zeng,
• Abstract summary: Global human motion reconstruction from in-the-wild monocular videos is increasingly demanded across VR, graphics, and robotics applications.<n>We present WATCH (World-aware Allied Trajectory and pose reconstruction for Camera and Human), a unified framework addressing both challenges.<n>Our work demonstrates the effectiveness of jointly modeling camera-human motion relationships and offers new insights for addressing the long-standing challenge of camera translation integration in global human motion reconstruction.
• Score: 14.608329202942057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Global human motion reconstruction from in-the-wild monocular videos is increasingly demanded across VR, graphics, and robotics applications, yet requires accurate mapping of human poses from camera to world coordinates-a task challenged by depth ambiguity, motion ambiguity, and the entanglement between camera and human movements. While human-motion-centric approaches excel in preserving motion details and physical plausibility, they suffer from two critical limitations: insufficient exploitation of camera orientation information and ineffective integration of camera translation cues. We present WATCH (World-aware Allied Trajectory and pose reconstruction for Camera and Human), a unified framework addressing both challenges. Our approach introduces an analytical heading angle decomposition technique that offers superior efficiency and extensibility compared to existing geometric methods. Additionally, we design a camera trajectory integration mechanism inspired by world models, providing an effective pathway for leveraging camera translation information beyond naive hard-decoding approaches. Through experiments on in-the-wild benchmarks, WATCH achieves state-of-the-art performance in end-to-end trajectory reconstruction. Our work demonstrates the effectiveness of jointly modeling camera-human motion relationships and offers new insights for addressing the long-standing challenge of camera translation integration in global human motion reconstruction. The code will be available publicly.

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