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Human pose and shape estimation (HPS) has attracted increasing attention inrecent years. While most existing studies focus on HPS from 2D images or videoswith inherent depth ambiguity, there are surging need to investigate HPS from3D point clouds as depth sensors have been frequently employed in commercialdevices. However, real-world sensory 3D points are usually noisy andincomplete, and also human bodies could have different poses of high diversity.To tackle these challenges, we propose a principled framework, PointHPS, foraccurate 3D HPS from point clouds captured in real-world settings, whichiteratively refines point features through a cascaded architecture.Specifically, each stage of PointHPS performs a series of downsampling andupsampling operations to extract and collate both local and global cues, whichare further enhanced by two novel modules: 1) Cross-stage Feature Fusion (CFF)for multi-scale feature propagation that allows information to flow effectivelythrough the stages, and 2) Intermediate Feature Enhancement (IFE) forbody-aware feature aggregation that improves feature quality after each stage.To facilitate a comprehensive study under various scenarios, we conduct ourexperiments on two large-scale benchmarks, comprising i) a dataset thatfeatures diverse subjects and actions captured by real commercial sensors in alaboratory environment, and ii) controlled synthetic data generated withrealistic considerations such as clothed humans in crowded outdoor scenes.Extensive experiments demonstrate that PointHPS, with its powerful pointfeature extraction and processing scheme, outperforms State-of-the-Art methodsby significant margins across the board. Homepage:https://caizhongang.github.io/projects/PointHPS/.

PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds