Hand in Silhouette: Synthetic-to-Real Multi-View Hand Reconstruction via Binary Masks

Calibrated multi-view hand reconstruction has advanced rapidly, yet the effectiveness of existing methods depends strongly on real captured data, which is costly to acquire, limited in hand-geometry diversity, and often affected by imperfect annotations. Synthetic data offers a scalable alternative with accurate supervision, but a substantial synthetic-to-real appearance gap hinders its use. To address this, we introduce **Hand in Silhouette (SilHand)**, a calibrated multi-view hand reconstruction framework that operates on binary hand masks—obtainable at low cost either automatically or manually—to reduce the synthetic-to-real observation gap. Rather than relying on RGB features for 3D lifting, SilHand projects metric 3D hypotheses into each view to query and fuse mask-ray evidence for hand reconstruction. This mask-centric formulation enables training on a large-scale synthetic dataset comprising 46.8k grasping poses and 60k free-hand samples, providing broad coverage of hand geometry while directly transferring to real captured hand geometry. Across five real-world benchmarks, SilHand improves relative reconstruction accuracy from **49.2%** to **63.4%** using masks rendered from reference hands. Moreover, reconstruction accuracy improves consistently as the synthetic training set grows, indicating that broader geometric coverage translates into better real-world performance. While achieving such diversity is difficult with real captured data, it becomes practical under our mask-centric formulation. Finally, SilHand remains robust under mask corruption, with reconstruction error increasing by at most 1.63 mm. Together, these results support binary masks as an effective interface for learning hand reconstruction from synthetic geometry.