Hand Pose Estimation via Latent 2.5 D Heatmap Regression

Estimating the 3D pose of a hand is an essential part of human-computer interaction. Estimating 3D pose using depth or multi- view sensors has become easier with recent advances in computer vision, however, regressing pose from a single RGB image is much less straight- forward. The main difficulty arises from the fact that 3D pose requires some form of depth estimates, which are ambiguous given only an RGB image. In this paper we propose a new method for 3D hand pose estima- tion from a monocular image through a novel 2.5D pose representation. Our new representation estimates pose up to a scaling factor, which can be estimated additionally if a prior of the hand size is given. We im- plicitly learn depth maps and heatmap distributions with a novel CNN architecture. Our system achieves state-of-the-art accuracy for 2D and 3D hand pose estimation on several challenging datasets in presence of severe occlusions.