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Analysis of hand-hand interactions is a crucial step towards betterunderstanding human behavior. However, most researches in 3D hand poseestimation have focused on the isolated single hand case. Therefore, we firstlypropose (1) a large-scale dataset, InterHand2.6M, and (2) a baseline network,InterNet, for 3D interacting hand pose estimation from a single RGB image. Theproposed InterHand2.6M consists of \textbf{2.6M labeled single and interactinghand frames} under various poses from multiple subjects. Our InterNetsimultaneously performs 3D single and interacting hand pose estimation. In ourexperiments, we demonstrate big gains in 3D interacting hand pose estimationaccuracy when leveraging the interacting hand data in InterHand2.6M. We alsoreport the accuracy of InterNet on InterHand2.6M, which serves as a strongbaseline for this new dataset. Finally, we show 3D interacting hand poseestimation results from general images. Our code and dataset are available athttps://mks0601.github.io/InterHand2.6M/.

InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image