DTDMat: A Comprehensive SVBRDF Dataset with Detailed Text Descriptions
Mufan Chen, Yanxiang Wang, Detao Hu, Pengfei Zhu, Jie Jassic Guo, Yanwen Guo · 2024
In this paper, we designed an automatic annotation tool to generate full descriptions to solve material datasets lacking essential text information challenge. This tool can extract six aspect tags from BRDF maps: intrinsic type, texture, color, roughness, lightness, and other relevant attributes. We applied this tool to both open-source material datasets and our own dataset to create DTDMat, which consists of 14,919 high-resolution Physically Based Rendering materials, each accompanied by a detailed text description. DTDMat covers 20 intrinsic material types and 22 texture structures. It stands out as the most diverse dataset in this domain and represents the largest texture dataset with associated text, offering a wide range of categories and diverse descriptions. We then trained a text-to-material generation framework based on DTDMat, yielding multiple generated BRDF maps that satisfy the input text.