HumanCrafter: Synergizing Generalizable Human Reconstruction and Semantic 3D Segmentation

• URL: http://arxiv.org/abs/2511.00468v1
• Date: Sat, 01 Nov 2025 09:29:36 GMT
• Title: HumanCrafter: Synergizing Generalizable Human Reconstruction and Semantic 3D Segmentation
• Authors: Panwang Pan, Tingting Shen, Chenxin Li, Yunlong Lin, Kairun Wen, Jingjing Zhao, Yixuan Yuan,
• Abstract summary: We propose a unified framework that enables the joint modeling of appearance and human-part semantics from a single image.<n>HumanCrafter surpasses existing state-of-the-art methods in both 3D human-part segmentation and 3D human reconstruction from a single image.
• Score: 51.27178551863772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in generative models have achieved high-fidelity in 3D human reconstruction, yet their utility for specific tasks (e.g., human 3D segmentation) remains constrained. We propose HumanCrafter, a unified framework that enables the joint modeling of appearance and human-part semantics from a single image in a feed-forward manner. Specifically, we integrate human geometric priors in the reconstruction stage and self-supervised semantic priors in the segmentation stage. To address labeled 3D human datasets scarcity, we further develop an interactive annotation procedure for generating high-quality data-label pairs. Our pixel-aligned aggregation enables cross-task synergy, while the multi-task objective simultaneously optimizes texture modeling fidelity and semantic consistency. Extensive experiments demonstrate that HumanCrafter surpasses existing state-of-the-art methods in both 3D human-part segmentation and 3D human reconstruction from a single image.

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