DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models
Yuqing Zhang, Yuan Liu, Zhiyu Xie, Lei Yang, Zhongyuan Liu, Mengzhou, Yang, Runze Zhang, Qilong Kou, Cheng Lin, Wenping Wang, Xiaogang Jin

TL;DR
DreamMat is a novel method that generates high-quality, physically-based rendering (PBR) materials from text descriptions by incorporating geometry and lighting awareness, overcoming limitations of traditional diffusion models.
Contribution
It introduces a light-aware diffusion model fine-tuned for accurate PBR material generation, addressing incorrect material decomposition issues in existing approaches.
Findings
Produces PBR materials with better visual quality
Achieves more accurate material decomposition
Outperforms baseline methods in rendering quality
Abstract
2D diffusion model, which often contains unwanted baked-in shading effects and results in unrealistic rendering effects in the downstream applications. Generating Physically Based Rendering (PBR) materials instead of just RGB textures would be a promising solution. However, directly distilling the PBR material parameters from 2D diffusion models still suffers from incorrect material decomposition, such as baked-in shading effects in albedo. We introduce DreamMat, an innovative approach to resolve the aforementioned problem, to generate high-quality PBR materials from text descriptions. We find out that the main reason for the incorrect material distillation is that large-scale 2D diffusion models are only trained to generate final shading colors, resulting in insufficient constraints on material decomposition during distillation. To tackle this problem, we first finetune a new…
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Taxonomy
TopicsPhotopolymerization techniques and applications
MethodsDiffusion
