VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting
Feitong Tan, Sean Fanello, Abhimitra Meka, Sergio Orts‐Escolano, Danhang Tang, Rohit Pandey, Jonathan Taylor, Ping Tan, Yinda Zhang
Abstract
We propose VoLux-GAN, a generative framework to synthesize 3D-aware faces with convincing relighting. Our main contribution is a volumetric HDRI relighting method that can efficiently accumulate albedo, diffuse and specular lighting contributions along each 3D ray for any desired HDR environmental map. Additionally, we show the importance of supervising the image decomposition process using multiple discriminators. In particular, we propose a data augmentation technique that leverages recent advances in single image portrait relighting to enforce consistent geometry, albedo, diffuse and specular components. Multiple experiments and comparisons with other generative frameworks show how our model is a step forward towards photorealistic relightable 3D generative models. Code and pre-trained models are available at: https://github.com/google/volux-gan.