Motion Capture of Hands in Action using Discriminative Salient Points
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Capturing the motion of two hands interacting with an object is a very challenging task due to the large number of degrees of freedom, selfocclusions, and similarity between the fingers, even in the case of multiple cameras observing the scene. In this paper we propose to use discriminatively learned salient points on the fingers and to estimate the finger-salient point associations simultaneously with the estimation of the hand pose. We introduce a differentiable objective function that also takes edges, optical flow and collisions into account. Our qualitative and quantitative evaluations show that the proposed approach achieves very accurate results for several challenging sequences containing hands and objects in action.
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