IntrinsiX

IntrinsiX: High-Quality PBR Generation using Image Priors

Recent generative models directly create shaded images, without any explicit material and shading representation. From text input, we generate renderable PBR maps. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation.

https://peter-kocsis.github.io/IntrinsiX/

Citation

If you find our code or paper useful, please cite as

@article
{kocsis2025intrinsix,
author = {Kocsis, Peter and H\"{o}llein, Lukas and Nie\{ss}ner, Matthias},
title = {IntrinsiX: High-Quality PBR Generation using Image Priors},
journal = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2025},
}