@article{Dang2025, 
author = {Ziqiang Dang and Wenqi Dong and Zesong Yang and Bangbang Yang and Liang Li and Yuewen Ma and Zhaopeng Cui},
title = {TexPro: Text-guided PBR texturing with procedural material modeling},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {4},
pages = {745-761},
keywords = {3D asset creation, text-guided texturing, procedural materials, multi-modal large language model (MLLM)},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450489},
doi = {10.26599/CVM.2025.9450489},
abstract = {In this paper, we present TexPro, a novel method for high-fidelity material generation for input 3D meshes given text prompts. Unlike existing text-conditioned texture generation methods that typically generate RGB textures with baked lighting, TexPro is able to produce diverse texture maps via procedural material modeling, which enables physically-based rendering, relighting, and additional benefits inherent to procedural materials. Specifically, we first generate multi-view reference images given the input textual prompt by employing the latest text-to-image model. We then derive texture maps through rendering-based optimization with recent differentiable procedural materials. To this end, we design several techniques to handle the misalignment between the generated multi-view images and 3D meshes, and introduce a novel material agent that enhances material classification and matching by exploring both part-level understanding and object-aware material reasoning. Experiments demonstrate the superiority of the proposed method over existing SOTAs, and its capability of relighting.}
}