MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911483348123648 |
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| author | Luo, Di Yang, Shuhui Yang, Mingxin Lu, Jiawei Tang, Yixuan Han, Xintong Chen, Zhuo Wang, Beibei Guo, Chunchao |
| author_facet | Luo, Di Yang, Shuhui Yang, Mingxin Lu, Jiawei Tang, Yixuan Han, Xintong Chen, Zhuo Wang, Beibei Guo, Chunchao |
| contents | Physically-based rendering (PBR) materials are fundamental to photorealistic graphics, yet their creation remains labor-intensive and requires specialized expertise. While generative models have advanced material synthesis, existing methods lack a unified representation bridging natural image appearance and PBR properties, leading to fragmented task-specific pipelines and inability to leverage large-scale RGB image data. We present MatPedia, a foundation model built upon a novel joint RGB-PBR representation that compactly encodes materials into two interdependent latents: one for RGB appearance and one for the four PBR maps encoding complementary physical properties. By formulating them as a 5-frame sequence and employing video diffusion architectures, MatPedia naturally captures their correlations while transferring visual priors from RGB generation models. This joint representation enables a unified framework handling multiple material tasks--text-to-material generation, image-to-material generation, and intrinsic decomposition--within a single architecture. Trained on MatHybrid-410K, a mixed corpus combining PBR datasets with large-scale RGB images, MatPedia achieves native $1024\times1024$ synthesis that substantially surpasses existing approaches in both quality and diversity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis Luo, Di Yang, Shuhui Yang, Mingxin Lu, Jiawei Tang, Yixuan Han, Xintong Chen, Zhuo Wang, Beibei Guo, Chunchao Computer Vision and Pattern Recognition Physically-based rendering (PBR) materials are fundamental to photorealistic graphics, yet their creation remains labor-intensive and requires specialized expertise. While generative models have advanced material synthesis, existing methods lack a unified representation bridging natural image appearance and PBR properties, leading to fragmented task-specific pipelines and inability to leverage large-scale RGB image data. We present MatPedia, a foundation model built upon a novel joint RGB-PBR representation that compactly encodes materials into two interdependent latents: one for RGB appearance and one for the four PBR maps encoding complementary physical properties. By formulating them as a 5-frame sequence and employing video diffusion architectures, MatPedia naturally captures their correlations while transferring visual priors from RGB generation models. This joint representation enables a unified framework handling multiple material tasks--text-to-material generation, image-to-material generation, and intrinsic decomposition--within a single architecture. Trained on MatHybrid-410K, a mixed corpus combining PBR datasets with large-scale RGB images, MatPedia achieves native $1024\times1024$ synthesis that substantially surpasses existing approaches in both quality and diversity. |
| title | MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16957 |