MIKU: Material Invariance and Knowledge Unification for PBR Generation

Researchers behind the arXiv paper 2611.07354v1, titled MIKU: Material Invariance and Knowledge Unification for PBR Generation, proposed a unified framework that standardizes physically based rendering (PBR) asset representation and generates physically consistent materials across 3D content domains. MIKU pairs a normalization scheme that harmonizes heterogeneous material parameterizations in a common representation space with a two-stage, physically aware diffusion model: geometry-aware albedo generation first synthesizes multi-view albedo maps under explicit geometric guidance, then metallic-roughness generation predicts roughness and metallic maps conditioned on that albedo. The authors report state-of-the-art texture quality and semantic consistency across object parts, addressing instability and physical implausibility caused by differing material definitions, rendering conventions and parameterizations.

arXiv:2611.07354v1 Announce Type: new Abstract: High-quality material generation for physically based rendering PBR remains challenging because datasets and synthesis pipelines lack standardized representations. Differences in material definitions, rendering conventions, and parameterizations lead to unstable and physically implausible results. We propose MIKU, a unified framework for standardized PBR asset representation and physically consistent material generation across diverse 3D content domains. Its core is a unified processing pipeline that standardizes the workflow from data preprocessing to final texture synthesis, maintaining consistent geometry and texture representations. A normalization scheme harmonizes heterogeneous material parameterizations in a common representation space, enabling robust cross-dataset training and inference. Building on this formulation, we train a novel physically aware diffusion model for PBR texture generation within a two-stage multi-view synthesis framework. First, geometry-aware albedo generation synthesizes multi-view albedo maps under explicit geometric guidance to ensure cross-view appearance consistency. Second, metallic-roughness generation uses the diffusion model to predict physically plausible roughness and metallic maps conditioned on the generated albedo and unified representation. Trained end-to-end on standardized data, the model learns to generate materials that respect category-level material priors and semantic part boundaries. By decoupling representation standardization from appearance synthesis, MIKU substantially improves material generation stability and physical consistency. Extensive experiments demonstrate state-of-the-art texture quality and semantic consistency across object parts, advancing automated material generation for 3D content creation.