We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3D pose can be easily introduced and significantly improves the accuracy and reliability of the predictions. We also show how to use context efficiently to deal with ambiguities between fingers. These two contributions allow us to significantly outperform the state-of-the-art on several challenging benchmarks, both in terms of accuracy and computation times.
Oberweger et al. REF have introduced and evaluated several architectures for CNNs to predict the 3D joint locations of a hand given a depth map.
Hands Deep in Deep Learning for Hand Pose Estimation
In Proceedings of 20th Computer Vision Winter Workshop (CVWW) 2015, pp. 21-30
Computer Science