Human pose and shape estimation (HPS) has attracted increasing attention in
recent years. While most existing studies focus on HPS from 2D images or videos
with inherent depth ambiguity, there are surging need to investigate HPS from
3D point clouds as depth sensors have been frequently employed in commercial
devices. However, real-world sensory 3D points are usually noisy and
incomplete, and also human bodies could have different poses of high diversity.
To tackle these challenges, we propose a principled framework, PointHPS, for
accurate 3D HPS from point clouds captured in real-world settings, which
iteratively refines point features through a cascaded architecture.
Specifically, each stage of PointHPS performs a series of downsampling and
upsampling operations to extract and collate both local and global cues, which
are further enhanced by two novel modules: 1) Cross-stage Feature Fusion (CFF)
for multi-scale feature propagation that allows information to flow effectively
through the stages, and 2) Intermediate Feature Enhancement (IFE) for
body-aware feature aggregation that improves feature quality after each stage.
To facilitate a comprehensive study under various scenarios, we conduct our
experiments on two large-scale benchmarks, comprising i) a dataset that
features diverse subjects and actions captured by real commercial sensors in a
laboratory environment, and ii) controlled synthetic data generated with
realistic considerations such as clothed humans in crowded outdoor scenes.
Extensive experiments demonstrate that PointHPS, with its powerful point
feature extraction and processing scheme, outperforms State-of-the-Art methods
by significant margins across the board. Homepage:
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基于 3D 点云数据的 PointHPS 框架通过迭代的特征提取和处理方案，在人体姿势和形状估计的研究领域中具有显著优势。