Carlos Aliaga

Shizhan Zhu, Shunsuke Saito, Aljaz Bozic et al.

Image-based lighting (IBL) is a widely used technique that renders objects using a high dynamic range image or environment map. However, aggregating the irradiance at the object's surface is computationally expensive, in particular for non-opaque, translucent materials that require volumetric rendering techniques. In this paper we present a fast neural 3D reconstruction and relighting model that extends volumetric implicit models such as neural radiance fields to be relightable using IBL. It is general enough to handle materials that exhibit complex light transport effects, such as translucency and glossy reflections from detailed surface geometry, producing realistic and compelling results. Rendering can be within a second at 800$\times$800 resolution (0.72s on an NVIDIA 3090 GPU and 0.30s on an A100 GPU) without engineering optimization. Our code and dataset are available at https://zhusz.github.io/TRHM-Webpage/.

Carlos Aliaga, Christophe Hery, Mengqi Xia

We present a new method to reconstruct and manipulate the spectral properties of human skin from simple RGB albedo captures. To this end, we leverage Monte Carlo light simulation over an accurate biophysical human skin layering model parameterized by its most important components, thereby covering a plausible range of colors. The practical complexity of the model allows us to learn the inverse mapping from any albedo to its most probable associated skin properties. Our technique can faithfully reproduce any skin type, being expressive enough to automatically handle more challenging areas like the lips or imperfections in the face. Thanks to the smoothness of the skin parameters maps recovered, the albedo can be robustly edited through meaningful biophysical properties.