Paper Info
Title
Handvoxnet: Deep Voxel-Based Network For 3d Hand Shape And Pose Estimation From A Single Depth Map
Abstract
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly' regress 3D hand meshes from 2D depth images via 2D convolutional neural networks, which leads to artefacts in the estimations due to perspective distortions in the images.In contrast, we propose a novel architecture with 3D convolutions trained in a weakly-supervised manner. The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations. The first one is the 3D voxelized grid of the shape which is accurate but does not preserve the mesh topology and the number of mesh vertices. The second representation is the 3D hand surface which is less accurate but does not suffer from the limitations of the first representation. We combine the advantages of these two representations by registering the hand surface to the voxelized hand shape. In the extensive experiments, the proposed approach improves over the state of the art by 47.8% on the SynHand5M dataset. Moreover, our augmentation policy for voxelized depth maps further enhances the accuracy of 3D hand pose estimation on real data. Our method produces visually more reasonable and realistic hand shapes on NYU and BigHand2.2M datasets compared to the existing approaches.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00714
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Malik Jameel100.34
Abdelaziz Ibrahim200.34
A. Elhayek3264.85
Soshi Shimada432.80
Sk Aziz Ali531.39
Vladislav Golyanik62212.55
Christian Theobalt73211159.16
Didier Stricker81266138.03