Recent hand pose estimation methods require large numbers of annotated training data to extract the dynamic information from a hand representation. Nevertheless, precise and dense annotation on the real data is difficult to come by and the amount of information passed to the training algorithm is significantly higher. This paper presents an approach to developing a hand pose estimation system which can accurately regress a 3D pose in an unsupervised manner. The whole process is performed in three stages. Firstly, the hand is modelled by a novel latent tree dependency model (LTDM) which transforms internal joints location to an explicit representation. Secondly, we perform predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. A mapping is then performed between an image depth and a generated representation. Thirdly, the hand joints are regressed using convolutional neural networks to finally estimate the latent pose given some depth map. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. To demonstrate the performance of the proposed system, a complete experiment is conducted on three challenging public datasets, ICVL, MSRA, and NYU. The empirical results show the significant performance of our method which is comparable or better than state-of-the-art approaches.
Learning hand latent features for unsupervised 3D hand pose estimation
