多视角融合提升穿衣服人体3D重建精度，无需重新训练模型。
MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
- 用多视角联合优化单一人体模型，保证视角一致性。
- 通过法向图融合，精准还原衣物褶皱和发型细节。
- 相比单视角方法显著提升重建质量，适合快速生成高保真人体模型。
本文提出MExECON，一种从稀疏多视角RGB图像重建穿衣服人体3D模型的新流程。基于单视角方法ECON，MExECON扩展其能力以利用多个视角，提升几何形状与身体姿态估计。核心是提出的联合多视角人体优化（JMBO）算法，该算法在所有输入视角上联合拟合单一SMPL-X人体模型，强制多视角一致性。优化后的人体模型作为低频先验，指导后续表面重建，通过法向图集成添加几何细节。MExECON融合前视与后视的法向图，准确捕捉衣物褶皱、发型等细粒度表面特征。所有多视角优势均无需网络重训练即可获得。实验表明，MExECON在保真度上持续优于单视角基线，并达到与现代少样本3D重建方法相当的性能。
原文摘要 · Abstract (English)
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.
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