This paper proposes a robust solution for accurate 3D hand pose estimation in the presence of an external object interacting with hands. Our main insight is that the shape of an object causes a configuration of the hand in the form of a hand grasp. Along this line, we simultaneously train deep neural networks using paired depth images. The object-oriented network learns functional grasps from an object perspective, whereas the hand-oriented network explores the details of hand configurations from a hand perspective. The two networks share intermediate observations produced from different perspectives to create a more informed representation. Our system then collaboratively classifies the grasp types and orientation of the hand and further constrains a pose space using these estimates. Finally, we collectively refine the unknown pose parameters to reconstruct the final hand pose. To this end, we conduct extensive evaluations to validate the efficacy of the proposed collaborative learning approach by comparing it with self-generated baselines and the state-of-the-art method.
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