Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting
Taeho Kang Youngki Lee
We present EgoTAP, a heatmap-to-3D pose lifting method for highly accurate
stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view
limbs in egocentric camera views make accurate pose estimation a challenging
problem. To address the challenge, prior methods employ joint
heatmaps-probabilistic 2D representations of the body pose, but heatmap-to-3D
pose conversion still remains an inaccurate process. We propose a novel
heatmap-to-3D lifting method composed of the Grid ViT Encoder and the
Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into
effective feature embedding using self-attention. Then, the Propagation Network
estimates the 3D pose by utilizing skeletal information to better estimate the
position of obscure joints. Our method significantly outperforms the previous
state-of-the-art qualitatively and quantitatively demonstrated by a 23.9\%
reduction of error in an MPJPE metric. Our source code is available in GitHub.