IntrinsicAvatar：通过显式光线追踪从单目视频实现动态人体的基于物理的逆渲染
IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing
AI总结：
本研究提出IntrinsicAvatar方法，通过显式蒙特卡洛光线追踪建模体积散射过程，结合人体关节运动，从单目视频中恢复着装人体的高质量解耦几何与外观属性，无需有监督预训练，且能泛化到新姿态实现新光照下的动画。
摘要：
我们提出了IntrinsicAvatar，这是一种仅从单目视频中恢复着装人体化身的固有属性（包括几何形状、反照率、材质和环境光照）的新方法。近期基于人体的神经渲染技术的发展，使得仅通过单目视频就能高质量重建着装人体的几何形状与外观成为可能。然而，这些方法将反照率、材质、环境光照等固有属性烘焙到单一的纠缠神经表示中。另一方面，仅有少数研究致力于解决从单目视频估计着装人体的几何形状与解耦外观属性的问题，由于它们通过学习得到的MLP（多层感知机）来近似次级着色效果，通常在质量和解耦效果上表现有限。在本研究中，我们提出通过蒙特卡洛光线追踪显式建模次级着色效果。我们将着装人体的渲染过程建模为体积散射过程，并将光线追踪与人体关节运动相结合。我们的方法可以从单段单目视频中恢复着装人体的高质量几何形状、反照率、材质和光照属性，且无需使用真实材质进行有监督预训练。此外，由于我们显式建模了体积散射过程和光线追踪，我们的模型可以自然地泛化到新姿态，支持在新光照条件下对重建的化身进行动画制作。
英文摘要：
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.
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