{"ID":2838444,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16957","arxiv_id":"2511.16957","title":"MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis","abstract":"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.","short_abstract":"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 propert...","url_abs":"https://arxiv.org/abs/2511.16957","url_pdf":"https://arxiv.org/pdf/2511.16957v2","authors":"[\"Di Luo\",\"Shuhui Yang\",\"Mingxin Yang\",\"Jiawei Lu\",\"Yixuan Tang\",\"Xintong Han\",\"Zhuo Chen\",\"Beibei Wang\",\"Chunchao Guo\"]","published":"2025-11-21T05:16:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}