ARM: Appearance Reconstruction Model for Relightable 3D Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909392886038528 |
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| author | Feng, Xiang Yu, Chang Bi, Zoubin Shang, Yintong Gao, Feng Wu, Hongzhi Zhou, Kun Jiang, Chenfanfu Yang, Yin |
| author_facet | Feng, Xiang Yu, Chang Bi, Zoubin Shang, Yintong Gao, Feng Wu, Hongzhi Zhou, Kun Jiang, Chenfanfu Yang, Yin |
| contents | Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, processing appearance within the UV texture space. Unlike previous methods, ARM improves texture quality by explicitly back-projecting measurements onto the texture map and processing them in a UV space module with a global receptive field. To resolve ambiguities between material and illumination in input images, ARM introduces a material prior that encodes semantic appearance information, enhancing the robustness of appearance decomposition. Trained on just 8 H100 GPUs, ARM outperforms existing methods both quantitatively and qualitatively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ARM: Appearance Reconstruction Model for Relightable 3D Generation Feng, Xiang Yu, Chang Bi, Zoubin Shang, Yintong Gao, Feng Wu, Hongzhi Zhou, Kun Jiang, Chenfanfu Yang, Yin Computer Vision and Pattern Recognition Graphics Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, processing appearance within the UV texture space. Unlike previous methods, ARM improves texture quality by explicitly back-projecting measurements onto the texture map and processing them in a UV space module with a global receptive field. To resolve ambiguities between material and illumination in input images, ARM introduces a material prior that encodes semantic appearance information, enhancing the robustness of appearance decomposition. Trained on just 8 H100 GPUs, ARM outperforms existing methods both quantitatively and qualitatively. |
| title | ARM: Appearance Reconstruction Model for Relightable 3D Generation |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2411.10825 |