Robust 3D Hand Pose Estimation in Single Depth Images: From Single View CNN to Multi View CNNs
Articulated hand pose estimation plays an important role in human computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high dimensional and non linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose to first project the query depth image onto three orthogonal planes and utilize these multi view projections to regress for 2D heat maps which estimate the joint positions on each plane. These multi view heat maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state of the art on a challenging dataset. Moreover, a cross dataset experiment also demonstrates the good generalization ability of the proposed method.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)