从单图重建带可调控发型的3D头像，解决头发建模语义不一致难题。
SRM-Hair: Single Image Head Mesh Reconstruction via 3D Morphable Hair
- 用射线建模保持头发顶点语义一致，支持发型融合与修改。
- 在250+真人发丝扫描数据上训练，单图重建精度达当前最佳。
- 适合虚拟形象、动画制作与高保真发型渲染场景。
3D可变形模型（3DMMs）在3D角色动画与重建中扮演基础性角色，但将3DMM扩展至头发仍具挑战，主要因不同发型间难以保持顶点级语义一致性。本文提出一种新方法——语义一致的射线建模头发（SRM-Hair），使头发可被系数控制且具备可变形性。核心创新在于语义一致的射线建模，能提取有序的头发表面顶点，具备发型融合加性、适应性、翻转与厚度调节等特性。我们收集了超过250个高保真真人发丝扫描数据，并配对3D面部数据，作为3D可变形头发的先验。基于此，SRM-Hair可仅凭单张图像重建出与3D头部结合的头发网格。该方法生成独立的头发网格，适用于虚拟形象创建、真实感动画与高保真头发渲染。定量与定性实验表明，SRM-Hair在3D网格重建任务上达到当前最优性能。项目代码已开源：https://github.com/wang-zidu/SRM-Hair。
原文摘要 · Abstract (English)
3D Morphable Models (3DMMs) have played a pivotal role as a fundamental representation or initialization for 3D avatar animation and reconstruction. However, extending 3DMMs to hair remains challenging due to the difficulty of enforcing vertex-level consistent semantic meaning across hair shapes. This paper introduces a novel method, Semantic-consistent Ray Modeling of Hair (SRM-Hair), for making 3D hair morphable and controlled by coefficients. The key contribution lies in semantic-consistent ray modeling, which extracts ordered hair surface vertices and exhibits notable properties such as additivity for hairstyle fusion, adaptability, flipping, and thickness modification. We collect a dataset of over 250 high-fidelity real hair scans paired with 3D face data to serve as a prior for the 3D morphable hair. Based on this, SRM-Hair can reconstruct a hair mesh combined with a 3D head from a single image. Note that SRM-Hair produces an independent hair mesh, facilitating applications in virtual avatar creation, realistic animation, and high-fidelity hair rendering. Both quantitative and qualitative experiments demonstrate that SRM-Hair achieves state-of-the-art performance in 3D mesh reconstruction. Our project is available at https://github.com/wang-zidu/SRM-Hair
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