EgoForce

Reconstructing the absolute 3D pose and shape of the hands from the user’s viewpoint using a single head-mounted camera is crucial for practical egocen- tric interaction in AR/VR, telepresence, and hand-centric manipulation tasks, where sensing must remain compact and unobtrusive. While monocular RGB methods have made progress, they remain constrained by depth–scale am- biguity and struggle to generalize across the diverse optical configurations of head-mounted devices. As a result, models typically require extensive training on device-specific datasets, which are costly and laborious to ac- quire. This paper addresses these challenges by introducing EgoForce , a monocular 3D hand reconstruction framework that recovers robust, absolute 3D hand pose and its position from the user’s (camera-space) viewpoint. EgoForce operates across fisheye, perspective, and distorted wide-FOV camera models using a single unified network. Our approach combines a differentiable forearm representation that stabilizes hand pose, a unified arm–hand transformer that predicts both hand and forearm geometry from a single egocentric view, mitigating depth–scale ambiguity, and a ray space closed-form solver that enables absolute 3D pose recovery across diverse head-mounted camera models. Experiments on three egocentric benchmarks show that EgoForce achieves state-of-the-art 3D accuracy, reducing camera- space MPJPE by up to 28% on the HOT3D dataset compared to prior methods and maintaining consistent performance across camera configurations.