Model Based Augmentation and Testing of an Annotated Hand Pose Dataset

Recent advances of deep learning technology enable one to train complex input-output mappings, provided that a high quality training set is available. In this paper, we show how to extend an existing dataset of depth maps of hand annotated with the corresponding 3D hand poses by fitting a 3D hand model to smart glove-based annotations and generating new hand views. We make available our code and the generated data. Based on the present procedure and our previous results, we suggest a pipeline for creating high quality data.

[1]  Vincent Lepetit,et al.  Training a Feedback Loop for Hand Pose Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Li Cheng,et al.  Efficient Hand Pose Estimation from a Single Depth Image , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Luc Van Gool,et al.  Smart particle filtering for 3D hand tracking , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[4]  Antonis A. Argyros,et al.  Tracking the articulated motion of two strongly interacting hands , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  E. Schneider,et al.  Real-time computer-based visual feedback improves visual acuity in downbeat nystagmus – a pilot study , 2016, Journal of NeuroEngineering and Rehabilitation.

[6]  Tae-Kyun Kim,et al.  Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Daniel Sonntag,et al.  LabelMovie: Semi-supervised machine annotation tool with quality assurance and crowd-sourcing options for videos , 2014, 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI).

[8]  Deva Ramanan,et al.  Understanding Everyday Hands in Action from RGB-D Images , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Vincent Lepetit,et al.  Hands Deep in Deep Learning for Hand Pose Estimation , 2015, ArXiv.

[10]  Marco Cuturi,et al.  Fast Global Alignment Kernels , 2011, ICML.

[11]  Yi Yang,et al.  Depth-Based Hand Pose Estimation: Data, Methods, and Challenges , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[12]  Tao Mei,et al.  Relaxing from Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Ken Perlin,et al.  Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , 2014, ACM Trans. Graph..

[14]  Thomas Brox,et al.  Learning to generate chairs with convolutional neural networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Teuvo Kohonen.  Adaptive Formation of Optimal Associative Mappings , 1977 .

[16]  Tapani Raiko,et al.  Semi-supervised Learning with Ladder Networks , 2015, NIPS.

[17]  Joan Bruna,et al.  Training Convolutional Networks with Noisy Labels , 2014, ICLR 2014.

[18]  Beomjoo Seo,et al.  Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded, randomized controlled trial , 2016, Journal of NeuroEngineering and Rehabilitation.

[19]  Horst Bischof,et al.  A Framework for Articulated Hand Pose Estimation and Evaluation , 2015, SCIA.

[20]  Jean Ponce,et al.  Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[22]  Xiangyu Zhu,et al.  High-fidelity Pose and Expression Normalization for face recognition in the wild , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Andrew W. Fitzgibbon,et al.  Accurate, Robust, and Flexible Real-time Hand Tracking , 2015, CHI.

[24]  Takeo Kanade,et al.  Spatio-temporal Event Classification Using Time-Series Kernel Based Structured Sparsity , 2014, ECCV.

[25]  Vincent Lepetit,et al.  Efficiently Creating 3D Training Data for Fine Hand Pose Estimation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  E. Rocon,et al.  Locomotor training through a novel robotic platform for gait rehabilitation in pediatric population: short report , 2016, Journal of NeuroEngineering and Rehabilitation.

[27]  Fei Han,et al.  Space-Time Representation of People Based on 3D Skeletal Data: A Review , 2016, Comput. Vis. Image Underst..