ReN Human: Learning Relightable Neural Implicit Surfaces for Animatable Human Rendering

Recently, implicit neural representation has been widely used to learn the appearance of human bodies in the canonical space, which can be further animated using a parametric human model. However, how to decompose the material properties from the implicit representation for relighting has not yet been investigated thoroughly. We propose to address this problem with a novel framework, ReN Human, that takes sparse or even monocular input videos collected in unconstrained lighting to produce a 3D human representation that can be rendered with novel views, poses, and lighting. Our method represents humans as deformable implicit neural representation and decomposes the geometry, material of humans as well as environment illumination for capturing a relightable and animatable human model. Moreover, we introduce a volumetric lighting grid consisting of spherical Gaussian mixtures to learn the spatially varying illumination and animatable visibility probes to model the dynamic self-occlusion caused by human motion. Specifically, we learn the material property fields and illumination using a physically-based rendering layer that uses Monte Carlo importance sampling to facilitate differentiation of the complex rendering integral. We demonstrate that our approach outperforms recent novel views and poses synthesis methods in a challenging benchmark with sparse videos, enabling high-fidelity human relighting.