HumanCrafter: Synergizing Generalizable Human Reconstruction and Semantic 3D Segmentation

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.

Authors

• Panwang Pan
• Tingting Shen
• Chenxin Li The Chinese University of Hong Kong
• Yunlong Lin Xiamen University
• Kairun Wen Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China School of Informatics,Xiamen University, China
• Jingjing Zhao
• Yixuan Yuan The Chinese University of Hong Kong

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