A Framework for Articulated Hand Pose Estimation and Evaluation.

We present in this paper a framework for articulated hand pose estimation and evaluation. Within this framework we implemented recently published methods for hand segmentation and inference of hand postures. We further propose a new approach for the segmentation and extend existing convolutional network based inference methods. Additionally, we created a new dataset that consists of a synthetically generated training set and accurately annotated test sequences captured with two different consumer depth cameras. The evaluation shows that we can improve with our methods the state-of-the-art. To foster further research, we will make all sources and the complete dataset used in this work publicly available.