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

Problem
This paper addresses the limitations of existing monocular RGB methods for 3D hand pose estimation from a user’s viewpoint, particularly in the context of augmented reality (AR), virtual reality (VR), and telepresence applications. Current approaches suffer from depth-scale ambiguity and lack generalization across various optical configurations of head-mounted devices, necessitating extensive training on device-specific datasets, which are resource-intensive to collect. This work presents EgoForce, a novel framework that aims to provide robust and absolute 3D hand pose reconstruction using a single egocentric camera, thereby filling a significant gap in the literature.

Method
EgoForce introduces a unified architecture that integrates several key components to enhance 3D hand pose estimation. The framework employs a differentiable forearm representation that stabilizes hand pose predictions, addressing depth-scale ambiguity. It utilizes an arm-hand transformer that jointly predicts the geometry of both the hand and forearm from a single egocentric view, allowing for improved spatial coherence. Additionally, a ray space closed-form solver is implemented to facilitate absolute 3D pose recovery across diverse camera models, including fisheye, perspective, and distorted wide-FOV configurations. The training process leverages a combination of synthetic and real-world datasets, although specific compute resources and training duration are not disclosed.

Results
EgoForce demonstrates state-of-the-art performance on three egocentric benchmarks, achieving a reduction in camera-space Mean Per Joint Position Error (MPJPE) by up to 28% on the HOT3D dataset compared to previous methods. The framework maintains consistent accuracy across different camera configurations, indicating its robustness and versatility. The results suggest significant improvements in 3D accuracy, although specific baseline methods and their performance metrics are not detailed in the abstract.

Limitations
The authors acknowledge that while EgoForce shows promising results, it may still be limited by the quality and diversity of the training data, particularly in real-world scenarios where occlusions and varying lighting conditions can affect performance. Additionally, the reliance on a single camera viewpoint may restrict the model’s ability to handle complex hand interactions that require multi-view data. The paper does not discuss potential computational overhead introduced by the differentiable forearm representation or the ray space solver.

Why it matters
The implications of EgoForce extend to various applications in AR/VR and telepresence, where accurate hand pose estimation is critical for user interaction and experience. By providing a robust solution that generalizes across different camera configurations, this work paves the way for more seamless integration of hand-centric manipulation tasks in immersive environments. Furthermore, the framework’s architecture could inspire future research in monocular 3D reconstruction and pose estimation, potentially leading to advancements in related fields such as robotics and human-computer interaction.

Authors: Christen Millerdurai, Shaoxiang Wang, Yaxu Xie, Vladislav Golyanik, Didier Stricker, Alain Pagani
Source: arXiv:2605.12498
URL: https://arxiv.org/abs/2605.12498v1