MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
This work addresses the problem of creating detailed 3D human avatars for applications like virtual reality or animation, but it is incremental as it builds on an existing method.
The paper tackles 3D reconstruction of clothed human avatars from sparse multi-view RGB images, extending a single-view method to improve geometry and pose estimation, achieving competitive performance with modern few-shot methods.
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.