{"ID":2837434,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18900","arxiv_id":"2511.18900","title":"MatMart: Material Reconstruction of 3D Objects via Diffusion","abstract":"Applying diffusion models to physically-based material estimation and generation has recently gained prominence. In this paper, we propose \\ttt, a novel material reconstruction framework for 3D objects, offering the following advantages. First, \\ttt\\ adopts a two-stage reconstruction, starting with accurate material prediction from inputs and followed by prior-guided material generation for unobserved views, yielding high-fidelity results. Second, by utilizing progressive inference alongside the proposed view-material cross-attention (VMCA), \\ttt\\ enables reconstruction from an arbitrary number of input images, demonstrating strong scalability and flexibility. Finally, \\ttt\\ achieves both material prediction and generation capabilities through end-to-end optimization of a single diffusion model, without relying on additional pre-trained models, thereby exhibiting enhanced stability across various types of objects. Extensive experiments demonstrate that \\ttt\\ achieves superior performance in material reconstruction compared to existing methods.","short_abstract":"Applying diffusion models to physically-based material estimation and generation has recently gained prominence. In this paper, we propose \\ttt, a novel material reconstruction framework for 3D objects, offering the following advantages. First, \\ttt\\ adopts a two-stage reconstruction, starting with accurate material pr...","url_abs":"https://arxiv.org/abs/2511.18900","url_pdf":"https://arxiv.org/pdf/2511.18900v1","authors":"[\"Xiuchao Wu\",\"Pengfei Zhu\",\"Jiangjing Lyu\",\"Xinguo Liu\",\"Jie Guo\",\"Yanwen Guo\",\"Weiwei Xu\",\"Chengfei Lyu\"]","published":"2025-11-24T08:58:14Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}