PoseSDF++: Point Cloud-Based 3-D Human Pose Estimation via Implicit Neural Representation

Predicting accurate human pose from 3-D visual observation presents a formidable challenge in computer vision, with numerous applications across various industries. However, most existing studies tackled this issue by regressing the 3-D pose from depth maps via 2-D convolutional neural networks or parametric human models, with limited development in point cloud-based methods. To this end, we propose PoseSDF++, i.e., a point cloud-based encoder–decoder network utilizing implicit neural representation to perform 3-D human pose estimation (HPE) and nonparametric shape reconstruction simultaneously. Leveraging the representative capacity of the signed distance function (SDF), we conceptualize the 3-D HPE as a multiple-shape reconstruction task and propose a distance-aware regression method to accurately estimate the 3-D joint positions. In specific, our PoseSDF++ consists of three modules: first, a hierarchical encoder with vector neuron layers extracts the multiscale rotation equivariant features from the point clouds captured from an arbitrary viewpoint, addressing the degradation issue caused by viewpoint variation of implicit representation; second, a shape decoder maps the extracted feature and the query to its corresponding shape SDF; third, a pose decoder computes the distance between the query and the target keypoints, namely, the pose SDF. Extensive experiments on four publicly available datasets demonstrate that our PoseSDF++ achieves competitive performance against the state-of-the-art point cloud-based methods and covering the human hand (HANDS 2019), lower limbs (ICL-Gait), and full body (DFAUST, LiDARHuman2.6M) pose estimation.