Existing works are either limited to simpler tracking settings ( e.g. Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applications, including AR/VR, robotics, or sign language recognition. Codes and data are available at interacting hand pose estimationDe-occlusionRemovalAmodal InterHand Dataset Experiments show that the proposed method significantly outperforms previous state-of-the-art interacting hand pose estimation approaches. 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. To tackle these two challenges, we propose a novel Hand De-occlusion and Removal (HDR) framework to perform hand de-occlusion and distractor removal. 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. In this way, it is straightforward to take advantage of the latest research progress on the single-hand pose estimation system. 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. Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions.
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