Human3R: Synergizing Generalizable Human Reconstruction and Semantic 3D Segmentation
Panwang Pan, Tingting Shen, Chenxin Li, Yunlong Lin, Kairun Wen, Jingjing Zhao, Yixuan Yuan
Recent advances in generative models have achieved high-fidelity in 3D human reconstruction, yet their utility for downstream applications (e.g., human 3D segmentation) remains constrained.  We propose $\textbf{Human3R}$, a unified framework that enables simultaneous modeling of appearance and human-part semantics in a $\textbf{generalizable}$ 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 Human3R surpasses existing state-of-the-art methods in both 3D human-part segmentation tasks and 3D human reconstruction. The ablation studies validate the efficacy of critical model designs. The entire pipeline (code and constructed dataset) will be publicly available.