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.
View on arXiv@article{dang2025_2410.15891,
title={ TexPro: Text-guided PBR Texturing with Procedural Material Modeling },
author={ Ziqiang Dang and Wenqi Dong and Zesong Yang and Bangbang Yang and Liang Li and Yuewen Ma and Zhaopeng Cui },
journal={arXiv preprint arXiv:2410.15891},
year={ 2025 }
}