StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions
This addresses the challenge of generating diverse illumination conditions for computer vision and graphics applications, though it is incremental as it builds on existing StyleGAN and intrinsic image methods.
The authors tackled the problem of relighting and resurfacing generated images without labeled data, achieving realistic lighting effects like cast shadows and glossy effects without paired or CGI data.
We propose a novel method, StyLitGAN, for relighting and resurfacing generated images in the absence of labeled data. Our approach generates images with realistic lighting effects, including cast shadows, soft shadows, inter-reflections, and glossy effects, without the need for paired or CGI data. StyLitGAN uses an intrinsic image method to decompose an image, followed by a search of the latent space of a pre-trained StyleGAN to identify a set of directions. By prompting the model to fix one component (e.g., albedo) and vary another (e.g., shading), we generate relighted images by adding the identified directions to the latent style codes. Quantitative metrics of change in albedo and lighting diversity allow us to choose effective directions using a forward selection process. Qualitative evaluation confirms the effectiveness of our method.