Neural LightRig: Unlocking Accurate Object Normal and Material
  Estimation with Multi-Light Diffusion
Authors: name: Zexin He, name: Tengfei Wang, name: Xin Huang, name: Xingang Pan, name: Ziwei Liu

Recovering the geometry and materials of objects from a single image is
challenging due to its under-constrained nature. In this paper, we present
Neural LightRig, a novel framework that boosts intrinsic estimation by
leveraging auxiliary multi-lighting conditions from 2D diffusion priors.
Specifically, 1) we first leverage illumination priors from large-scale
diffusion models to build our multi-light diffusion model on a synthetic
relighting dataset with dedicated designs. This diffusion model generates
multiple consistent images, each illuminated by point light sources in
different directions. 2) By using these varied lighting images to reduce
estimation uncertainty, we train a large G-buffer model with a U-Net backbone
to accurately predict surface normals and materials. Extensive experiments
validate that our approach significantly outperforms state-of-the-art methods,
enabling accurate surface normal and PBR material estimation with vivid
relighting effects. Code and dataset are available on our project page at
https://projects.zxhezexin.com/neural-lightrig.