Estimation of 3D human hand poses with structured pose prior

Abstract

Here, the authors present multistage estimation model embedding with structured pose prior (SPP), a novel coarse-to-fine framework for real-time 3D hand estimation from single depth image. Authors’ main contributions can be summarised as follows: (i) The authors proposed SPP to enforce constraints of canonical hand pose instead of original hand pose. (ii) The authors are the first to adopt under-complete stacked denoising auto-encoder (SDA) to construct pose prior by mapping canonical hand pose to latent representation. In the case of enforcing constraints of canonical hand pose, the authors empirically validate that under-complete SDA outperforms over-complete SDA in improving the hand estimation accuracy. (iii) The authors propose candidate keypoints patches (CKP) as intermediate data to conduct further hand pose refinement. Experimental evaluation on two publically available datasets shows that authors’ model is competitive both in accuracy and computation time. Especially, authors’ method placed first in the location of palm key-point on both two datasets, and the high accuracy of hand palm key-point plays an important role in many applications, such as that manipulator can grasp objects to specific coordinates with the guiding of human hand palm.