Simple and effective deep hand shape and pose regression from a single depth image. (December 2019)

• Record Type: Journal Article
• Authors: Malik, Jameel; Elhayek, Ahmed; Nunnari, Fabrizio; Stricker, Didier
• Is Part Of: Computers & graphics. Volume 85(2019)
• Page Start: 85
• Page End: 91
• Publication Date: 2019-12
• Subjects: Convolutional neural network (CNN) -- Depth image -- Three-dimensional (3D) hand mesh and pose; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2019.10.002
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: Real-time CNN-based approach for recovering hand shapes and poses using depth camera. First egocentric synthetic hand shape and pose dataset produced. Method achieved state-of-the-art accuracy with SynHand5M shape and pose dataset. Real hand shapes recovered from depth frames without real hand shape ground truth. Graphical abstract: Abstract: Simultaneously estimating the 3D shape and pose of a hand in real time is a new and challenging computer graphics problem, which is important for animation and interactions with 3D objects in virtual environments with personalized hand shapes. CNN-based direct hand pose estimation methods are the state-of-the-art approaches, but they can only regress a 3D hand pose from a single depth image. In this study, we developed a simple and effective real-time CNN-based direct regression approach for simultaneously estimating the 3D hand shape and pose, as well as structure constraints for both egocentric and third person viewpoints by learning from the synthetic depth. In addition, we produced the first million-scale egocentric synthetic dataset called SynHandEgo, which contains egocentric depth images with accurate shape and pose annotations, as well as color segmentation of the hand parts. Our network is trained based on combined real and synthetic datasets with full supervision of the hand pose and structure constraints, and semi-supervision of the hand mesh. Our approach performed better than the state-of-the-art methods based on the SynHand5M synthetic dataset in terms of both the 3D shape and pose recovery. By learning simultaneously using real and synthetic data, we demonstrated the feasibility of hand mesh recovery from two real hand pose datasets, i.e., BigHand2.2M and NYU. Moreover, our method obtained more accurate estimates of the 3D hand poses based on the NYU dataset compared with the existing methods that output more than joint positions. The SynHandEgo dataset has been made publicly available to promote further research in the emerging domain of hand shape and pose recovery from egocentric viewpoints (https://bit.ly/2WMWM5u ).