Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs
- 1 June 2016
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3593-3601
- https://doi.org/10.1109/cvpr.2016.391
Abstract
Articulated hand pose estimation plays an important role in human-computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high-dimensional and non-linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane. These multi-view heat-maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment also demonstrates the good generalization ability of the proposed method.Keywords
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This publication has 27 references indexed in Scilit:
- Robust Articulated‐ICP for Real‐Time Hand TrackingComputer Graphics Forum, 2015
- Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth dataPattern Recognition Letters, 2014
- Resolving Ambiguous Hand Pose Predictions by Exploiting Part CorrelationsIEEE Transactions on Circuits and Systems for Video Technology, 2014
- Real-Time Continuous Pose Recovery of Human Hands Using Convolutional NetworksACM Transactions on Graphics, 2014
- Latent Regression Forest: Structured Estimation of 3D Articulated Hand PosturePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Realtime and Robust Hand Tracking from DepthPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- DeepPose: Human Pose Estimation via Deep Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Parsing the Hand in Depth ImagesIEEE Transactions on Multimedia, 2014
- Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression ForestsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Efficient model-based 3D tracking of hand articulations using KinectPublished by British Machine Vision Association and Society for Pattern Recognition ,2011