MatMart: Material Reconstruction of 3D Objects via Diffusion
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918216317534208 |
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| author | Wu, Xiuchao Zhu, Pengfei Lyu, Jiangjing Liu, Xinguo Guo, Jie Guo, Yanwen Xu, Weiwei Lyu, Chengfei |
| author_facet | Wu, Xiuchao Zhu, Pengfei Lyu, Jiangjing Liu, Xinguo Guo, Jie Guo, Yanwen Xu, Weiwei Lyu, Chengfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18900 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MatMart: Material Reconstruction of 3D Objects via Diffusion Wu, Xiuchao Zhu, Pengfei Lyu, Jiangjing Liu, Xinguo Guo, Jie Guo, Yanwen Xu, Weiwei Lyu, Chengfei Graphics Computer Vision and Pattern Recognition 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. |
| title | MatMart: Material Reconstruction of 3D Objects via Diffusion |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18900 |