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We propose a novel method, StyLitGAN, for relighting and resurfacinggenerated images in the absence of labeled data. Our approach generates imageswith 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 asearch of the latent space of a pre-trained StyleGAN to identify a set ofdirections. By prompting the model to fix one component (e.g., albedo) and varyanother (e.g., shading), we generate relighted images by adding the identifieddirections to the latent style codes. Quantitative metrics of change in albedoand lighting diversity allow us to choose effective directions using a forwardselection process. Qualitative evaluation confirms the effectiveness of ourmethod.

StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions