StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions

Autor:	Bhattad, Anand, Forsyth, D. A.
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: https://anandbhattad.github.io/stylitgan/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.10351 Zobrazit plný text záznamu View this record from Arxiv