MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912546839068672 |
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| author | Uğur, Fulden Ece Redondo, Rafael Barreiro, Albert Hristov, Stefan Marí, Roger |
| author_facet | Uğur, Fulden Ece Redondo, Rafael Barreiro, Albert Hristov, Stefan Marí, Roger |
| contents | This work presents MExECON, a novel pipeline for 3D reconstruction of clothed human avatars from sparse multi-view RGB images. Building on the single-view method ECON, MExECON extends its capabilities to leverage multiple viewpoints, improving geometry and body pose estimation. At the core of the pipeline is the proposed Joint Multi-view Body Optimization (JMBO) algorithm, which fits a single SMPL-X body model jointly across all input views, enforcing multi-view consistency. The optimized body model serves as a low-frequency prior that guides the subsequent surface reconstruction, where geometric details are added via normal map integration. MExECON integrates normal maps from both front and back views to accurately capture fine-grained surface details such as clothing folds and hairstyles. All multi-view gains are achieved without requiring any network re-training. Experimental results show that MExECON consistently improves fidelity over the single-view baseline and achieves competitive performance compared to modern few-shot 3D reconstruction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15500 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration Uğur, Fulden Ece Redondo, Rafael Barreiro, Albert Hristov, Stefan Marí, Roger Computer Vision and Pattern Recognition This work presents MExECON, a novel pipeline for 3D reconstruction of clothed human avatars from sparse multi-view RGB images. Building on the single-view method ECON, MExECON extends its capabilities to leverage multiple viewpoints, improving geometry and body pose estimation. At the core of the pipeline is the proposed Joint Multi-view Body Optimization (JMBO) algorithm, which fits a single SMPL-X body model jointly across all input views, enforcing multi-view consistency. The optimized body model serves as a low-frequency prior that guides the subsequent surface reconstruction, where geometric details are added via normal map integration. MExECON integrates normal maps from both front and back views to accurately capture fine-grained surface details such as clothing folds and hairstyles. All multi-view gains are achieved without requiring any network re-training. Experimental results show that MExECON consistently improves fidelity over the single-view baseline and achieves competitive performance compared to modern few-shot 3D reconstruction methods. |
| title | MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.15500 |