EgoForce: Forearm-Guided Camera-Space 3D Hand Pose from a Monocular Egocentric Camera

TL;DR

EgoForce is a unified monocular framework that accurately reconstructs absolute 3D hand pose and position from a single egocentric camera across various optical configurations, advancing AR/VR interaction.

Contribution

It introduces a novel approach combining a differentiable forearm model, a unified transformer, and a ray space solver for robust, device-agnostic 3D hand reconstruction from monocular egocentric images.

Findings

Achieves state-of-the-art 3D accuracy on egocentric benchmarks.

Reduces camera-space MPJPE by up to 28% on HOT3D dataset.

Maintains consistent performance across diverse head-mounted camera models.

Abstract

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 egocentric 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 ambiguity 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 acquire. 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. For more details, visit the project page at https://dfki-av.github.io/EgoForce.