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.