Estimating 3D interacting hand pose from a single RGB image is essential for
understanding human actions. Unlike most previous works that directly predict
the 3D poses of two interacting hands simultaneously, we propose to decompose
the challenging interacting hand pose estimation task and estimate the pose of
each hand separately. In this way, it is straightforward to take advantage of
the latest research progress on the single-hand pose estimation system.
However, hand pose estimation in interacting scenarios is very challenging, due
to (1) severe hand-hand occlusion and (2) ambiguity caused by the homogeneous
appearance of hands. To tackle these two challenges, we propose a novel Hand
De-occlusion and Removal (HDR) framework to perform hand de-occlusion and
distractor removal. We also propose the first large-scale synthetic amodal hand
dataset, termed Amodal InterHand Dataset (AIH), to facilitate model training
and promote the development of the related research. Experiments show that the
proposed method significantly outperforms previous state-of-the-art interacting
hand pose estimation approaches. Codes and data are available at
this https URL

该研究提出了一种新的处理双手交互姿态估计的方法，包括手部去遮挡和干扰物去除，建立了大规模合成数据集以促进研究发展，并显示该方法显著优于先前的同类研究。