🤖 AI Summary
This work addresses the challenging problem of 3D human body reconstruction from sparse, uncalibrated multi-view RGB images depicting clothed subjects. Methodologically, we propose a retraining-free multi-view joint optimization framework built upon the SMPL-X parametric model. We design a cross-view consistent body optimization algorithm and introduce a front-back normal map integration mechanism to explicitly capture geometric details such as clothing wrinkles and hairstyles. Surface fidelity is further enhanced via multi-view joint fitting coupled with normal-guided geometric refinement. Experiments demonstrate that our method achieves superior reconstruction quality compared to single-view baselines—using only 2–4 uncalibrated views—and attains state-of-the-art performance among few-shot 3D human reconstruction approaches. The framework is both computationally efficient and highly generalizable across diverse clothing and pose configurations.
📝 Abstract
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.