Analysis of hand-hand interactions is a crucial step towards better
understanding human behavior. However, most researches in 3D hand pose
estimation have focused on the isolated single hand case. Therefore, we firstly
propose (1) a large-scale dataset, InterHand2.6M, and (2) a baseline network,
InterNet, for 3D interacting hand pose estimation from a single RGB image. The
proposed InterHand2.6M consists of \textbf{2.6M labeled single and interacting
hand frames} under various poses from multiple subjects. Our InterNet
simultaneously performs 3D single and interacting hand pose estimation. In our
experiments, we demonstrate big gains in 3D interacting hand pose estimation
accuracy when leveraging the interacting hand data in InterHand2.6M. We also
report the accuracy of InterNet on InterHand2.6M, which serves as a strong
baseline for this new dataset. Finally, we show 3D interacting hand pose
estimation results from general images. Our code and dataset are available at
this https URL

该篇论文针对手 - 手互动的行为研究提出了 InterNet 网络和 InterHand2.6M 数据库，用于从单个 RGB 图像中实现三维互动手势姿态估计，获得更准确的结果。