HySUP: Seam-Aware Hand-Body Fusion for Accurate Whole-Body 3-D Reconstruction from a Single Image

Expressive human pose and shape estimation (EHPS)—which jointly captures body, hand, and facial motion—is vital for emerging AR/VR and telepresence applications. State-of-the-art holistic regressors accurately recover overall body pose yet still yield fused or imprecise fingers, whereas hand-specific networks resolve fine articulation but sacrifice global coherence. We benchmark two leading holistic models (SMPLest-X, SMPLer-X) and two hand-specialized models (HaMeR, WiLoR) on the EHF dataset and confirm this trade-off. Leveraging their complementary strengths, we introduce HySUP—a lightweight pipeline that replaces a SMPLest-X hand region with HaMeR output through a two-stage seam-aware alignment: (i) a PCA-guided Procrustes transform for scale and pose normalization, followed by (ii) a rigid-as-possible refinement that enforces vertex- and normal-level consistency along the wrist seam. Without retraining and with only 150 ms of overhead, HySUP reduces hand PA-MPVPE and PA-MPJPE on EHF by 3.30 mm (27.11 %) and 3.55 mm (29.48 %), respectively, while preserving full-body accuracy. These results highlight a simple path forward: "plug-and-play" fusion of specialist parts can outperform ever-larger holistic models at a fraction of the computational cost. Code and more results can be found on our project page.