Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views

TL;DR

Ego3DPose introduces a novel binocular egocentric 3D pose estimation system that leverages stereo cues and perspective-aware representations to significantly improve accuracy in challenging scenarios.

Contribution

The paper presents a two-path network architecture and a perspective-aware representation to enhance 3D pose estimation from egocentric binocular views, addressing occlusion and bias issues.

Findings

Outperforms state-of-the-art models with 23.1% MPJPE reduction

Effective in challenging occlusion and self-occlusion scenarios

Demonstrates superior qualitative results across various scenarios

Abstract

We present Ego3DPose, a highly accurate binocular egocentric 3D pose reconstruction system. The binocular egocentric setup offers practicality and usefulness in various applications, however, it remains largely under-explored. It has been suffering from low pose estimation accuracy due to viewing distortion, severe self-occlusion, and limited field-of-view of the joints in egocentric 2D images. Here, we notice that two important 3D cues, stereo correspondences, and perspective, contained in the egocentric binocular input are neglected. Current methods heavily rely on 2D image features, implicitly learning 3D information, which introduces biases towards commonly observed motions and leads to low overall accuracy. We observe that they not only fail in challenging occlusion cases but also in estimating visible joint positions. To address these challenges, we propose two novel approaches. First, we design a two-path network architecture with a path that estimates pose per limb independently with its binocular heatmaps. Without full-body information provided, it alleviates bias toward trained full-body distribution. Second, we leverage the egocentric view of body limbs, which exhibits strong perspective variance (e.g., a significantly large-size hand when it is close to the camera). We propose a new perspective-aware representation using trigonometry, enabling the network to estimate the 3D orientation of limbs. Finally, we develop an end-to-end pose reconstruction network that synergizes both techniques. Our comprehensive evaluations demonstrate that Ego3DPose outperforms state-of-the-art models by a pose estimation error (i.e., MPJPE) reduction of 23.1% in the UnrealEgo dataset. Our qualitative results highlight the superiority of our approach across a range of scenarios and challenges.