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Accurate hand pose estimation at joint level has several uses on human-robot interaction, user interfacing and virtual reality applications. Yet, it currently is not a solved problem. The novel deep learning techniques could make a great improvement on this matter but they need a huge amount of annotated data. The hand pose datasets released so far present some issues that make them impossible to use on deep learning methods such as the few number of samples, high-level abstraction annotations or samples consisting in depth maps. In this work, we introduce a multiview hand pose dataset in which we provide color images of hands and different kind of annotations for each, i.e the bounding box and the 2D and 3D location on the joints in the hand. Besides, we introduce a simple yet accurate deep learning architecture for real-time robust 2D hand pose estimation.
Estimating 3D hand poses from a single RGB image is challenging because depth ambiguity leads the problem ill-posed. Training hand pose estimators with 3D hand mesh annotations and multi-view images often results in significant performance gains. How
Articulated hand pose estimation is a challenging task for human-computer interaction. The state-of-the-art hand pose estimation algorithms work only with one or a few subjects for which they have been calibrated or trained. Particularly, the hybrid
In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images with the same size of 1024*1024.
3D hand-object pose estimation is an important issue to understand the interaction between human and environment. Current hand-object pose estimation methods require detailed 3D labels, which are expensive and labor-intensive. To tackle the problem o
We propose a Bayesian approximation to a deep learning architecture for 3D hand pose estimation. Through this framework, we explore and analyse the two types of uncertainties that are influenced either by data or by the learning capability. Furthermo