Full-body ego-pose estimation from head and hand poses alone has become an
active area of research to power articulate avatar representation on
headset-based platforms. However, existing methods over-rely on the confines of
the motion-capture spaces in which datasets were recorded, while simultaneously
assuming continuous capture of joint motions and uniform body dimensions. In
this paper, we propose EgoPoser, which overcomes these limitations by 1)
rethinking the input representation for headset-based ego-pose estimation and
introducing a novel motion decomposition method that predicts full-body pose
independent of global positions, 2) robustly modeling body pose from
intermittent hand position and orientation tracking only when inside a
headset's field of view, and 3) generalizing across various body sizes for
different users. Our experiments show that EgoPoser outperforms
state-of-the-art methods both qualitatively and quantitatively, while
maintaining a high inference speed of over 600 fps. EgoPoser establishes a
robust baseline for future work, where full-body pose estimation needs no
longer rely on outside-in capture and can scale to large-scene environments.

本文提出了一种名为 EgoPoser 的方法，通过重新思考基于头戴式设备的自我姿态估计的输入表示以及引入一种新的运动分解方法，在不依赖全局位置的情况下预测全身姿态，仅通过头戴式设备的视野内的间歇性手部位置和方向跟踪鲁棒地建模身体姿势，并且针对不同用户的各种身体尺寸进行了泛化。实验证明，EgoPoser 在质量和数量上都胜过了现有的方法，并且推断速度高达每秒超过 600 帧。EgoPoser 为未来的工作奠定了坚实的基础，不再需要依赖外部捕捉并能在大场景环境中扩展全身姿势估计。