In this paper we propose a generate-and-estimate framework to produce high-quality SVBRDF channels for physically-based rendering of materials. In the generation stage, we leverage the diverse conditioning techniques of text-to-image models to synthesize tileable texture images with creative control. In the estimation stage, we introduce a chain-of-rendering-decomposition (Chord) scheme, which sequentially predicts SVBRDF channels by feeding previously extracted representations into a single-step, image-conditioned diffusion model. Our material estimation method demonstrates strong robustness on both generated textures and in-the-wild photographs. Moreover, we showcase the flexibility of our entire framework across diverse applications, including text-to-material, image-to-material, structure-guided generation, and material editing.
Stage 1: Tileable texture image ($ I_\text{RGB} $) generation using a fine-tuned diffusion model, controllable via user guidance (text prompts, reference images, or other control types).
Stage 2: Material estimation predicts SVBRDF channels sequentially:
$\dagger$: re-trained on our dataset, $\ast$: author-provided weights.
@inproceedings{ying2025chord,
author = {Ying, Zhi and Rong, Boxiang and Wang, Jingyu and Xu, Maoyuan},
title = {Chord: Chain of Rendering Decomposition for PBR Material Estimation from Generated Texture Images},
year = {2025},
isbn = {9798400721373},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3757377.3763848},
doi = {10.1145/3757377.3763848},
booktitle = {Proceedings of the SIGGRAPH Asia 2025 Conference Papers},
articleno = {164},
numpages = {11},
keywords = {Appearance Modeling, Material Generation, Texture Synthesis, SVBRDF, Image-conditional Diffusion Models},
series = {SA Conference Papers '25}
}