  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.
