PoseSDF: Simultaneous 3D Human Shape Reconstruction and Gait Pose Estimation Using Signed Distance Functions

Vision-based 3D human pose estimation and shape reconstruction play important roles in robot-assisted healthcare monitoring and personal assistance. However, 3D data captured from a single viewpoint always encounter occlusions and exhibit substantial heterogeneity across different views, resulting in significant challenges for both tasks. Extensive approaches have been proposed to perform each task separately, but few of them present a unified solution. In this paper, we propose a novel network based on signed distance functions, namely PoseSDF, to simultaneously reconstruct 3D lower limb shape and estimate gait pose by two dedicated branches. To promote multi-task learning, several strategies are developed to ensure that these two branches leverage the same latent shape code while exchanging information between them. More importantly, an auxiliary RotNet is incorporated into the inference phase, overcoming the inherent limitations of implicit neural functions under cross-view scenarios. Experimental results demonstrate that our proposed PoseSDF can achieve both high-quality shape reconstruction and precise pose estimation, generalizing well on the data from novel views, gait patterns, as well as real-world.