Speaker: Vincent Lepetit from TU Graz
Date: 06-July-2015
IRI Research Seminars
Abstract:
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. A recent arXiv paper independently shows that our approach outperforms the other ones on the existing benchmarks.
