Skip to yearly menu bar Skip to main content


StyLitGAN: Image-Based Relighting via Latent Control

Anand Bhattad · James Soole · David Forsyth

Arch 4A-E Poster #392
[ ] [ Project Page ]
Wed 19 Jun 10:30 a.m. PDT — noon PDT


We describe a novel method, StyLitGAN, for relighting and resurfacing images in the absence of labeled data. StyLitGAN 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 pretrained 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.

Live content is unavailable. Log in and register to view live content