We present IntrinsicAvatar, a novel approach to recovering the intrinsic properties of clothed human avatars including geometry, albedo, material, and environment lighting from only monocular videos. Recent advancements in human-based neural rendering have enabled high-quality geometry and appearance reconstruction of clothed humans from just monocular videos. However, these methods bake intrinsic properties such as albedo, material, and environment lighting into a single entangled neural representation. On the other hand, only a handful of works tackle the problem of estimating geometry and disentangled appearance properties of clothed humans from monocular videos. They usually achieve limited quality and disentanglement due to approximations of secondary shading effects via learned MLPs. In this work, we propose to model secondary shading effects explicitly via Monte-Carlo ray tracing. We model the rendering process of clothed humans as a volumetric scattering process, and combine ray tracing with body articulation. Our approach can recover high-quality geometry, albedo, material, and lighting properties of clothed humans from a single monocular video, without requiring supervised pre-training using ground truth materials. Furthermore, since we explicitly model the volumetric scattering process and ray tracing, our model naturally generalizes to novel poses, enabling animation of the reconstructed avatar in novel lighting conditions.

翻译：我们提出IntrinsicAvatar，一种从仅包含单目视频中恢复穿着衣物人体化身内在属性（包括几何、反照率、材质和光照环境）的新方法。近期基于人体神经渲染的进展已实现从单目视频中对穿着衣物人体进行高质量几何与外观重建。然而，这些方法将反照率、材质和光照环境等内在属性烘焙至单一纠缠的神经表征中。另一方面，仅少数研究工作尝试从单目视频中估计穿着衣物人体的几何与解耦外观属性。由于通过学习的MLP对次级阴影效果进行近似处理，它们通常只能达到有限的质量和解耦程度。在本工作中，我们提出通过蒙特卡洛光线追踪显式建模次级阴影效果。我们将穿着衣物人体的渲染过程建模为体散射过程，并将光线追踪与人体关节运动相结合。我们的方法可在无需使用真实材质进行监督预训练的条件下，从单个单目视频中恢复穿着衣物人体的高质量几何、反照率、材质和光照属性。此外，由于显式建模体散射过程与光线追踪，我们的模型自然泛化至新姿态，使得重建化身能够在新型光照条件下进行动画生成。