Part Segmentation of Human Meshes via Multi-View Human Parsing

• URL: http://arxiv.org/abs/2507.18655v2
• Date: Mon, 28 Jul 2025 01:06:32 GMT
• Title: Part Segmentation of Human Meshes via Multi-View Human Parsing
• Authors: James Dickens, Kamyar Hamad,
• Abstract summary: In parallel, the field of human parsing focuses on predicting body part and clothing/accessory labels from images.<n>This work aims to bridge these two domains by enabling per-vertex semantic segmentation of large-scale human meshes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in point cloud deep learning have led to models that achieve high per-part labeling accuracy on large-scale point clouds, using only the raw geometry of unordered point sets. In parallel, the field of human parsing focuses on predicting body part and clothing/accessory labels from images. This work aims to bridge these two domains by enabling per-vertex semantic segmentation of large-scale human meshes. To achieve this, a pseudo-ground truth labeling pipeline is developed for the Thuman2.1 dataset: meshes are first aligned to a canonical pose, segmented from multiple viewpoints, and the resulting point-level labels are then backprojected onto the original mesh to produce per-point pseudo ground truth annotations. Subsequently, a novel, memory-efficient sampling strategy is introduced, a windowed iterative farthest point sampling (FPS) with space-filling curve-based serialization to effectively downsample the point clouds. This is followed by a purely geometric segmentation using PointTransformer, enabling semantic parsing of human meshes without relying on texture information. Experimental results confirm the effectiveness and accuracy of the proposed approach.

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