PBR-GAN: Imitating Physically-Based Rendering With Generative Adversarial Networks

We propose a Generative Adversarial Network (GAN)-based architecture for achieving high-quality physically based rendering (PBR). Conventional PBR relies heavily on ray tracing, which is computationally expensive in complicated environments. Some recent deep learning-based methods can improve efficiency but cannot deal with illumination variation well. In this paper, we propose PBR-GAN, an end-to-end GAN-based network that solves these problems while generating natural photo-realistic images. Two encoders (the shading encoder and albedo encoder) and two decoders (the image decoder and light decoder) are introduced to achieve our target. The two encoders and the image decoder constitute the generator that learns the mapping between the generated domain and the real domain. The light decoder produces light maps that pay more attention to the highlight and shadow regions. The discriminator aims to optimize the generator by distinguishing target images from the generated ones. Three novel loss items, concentrating on domain translation, overall shading preservation, and light map estimation, are proposed to optimize the photo-realistic outputs. Furthermore, a real dataset is collected to provide realistic information for training GAN architecture. Extensive experiments indicate that PBR-GAN can preserve the illumination variation and improve the image perceptual quality.