Shanxin Yuan

TL;DR: This work collects RGB-D video sequences comprised of more than 100K frames of 45 daily hand action categories, involving 26 different objects in several hand configurations, and sees clear benefits of using hand pose as a cue for action recognition compared to other data modalities.

TL;DR: In this paper, the state-of-the-art 3D hand pose estimation from depth images is investigated, and the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions.

TL;DR: This paper proposes a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps captured with minimal restriction on the range of motion, and demonstrates significant improvements in cross-benchmark performance.

TL;DR: Zhang et al. as discussed by the authors proposed a hybrid hand pose estimation method by applying the kinematic hierarchy strategy to the input space (as well as the output space) of the discriminative method by a spatial attention mechanism and to the optimization of the generative algorithm by hierarchical Particle Swarm Optimization (PSO).

TL;DR: This work proposes a novel method that can effectively incorporate temporal information in a hierarchical way and achieves favorable performance against state-of-the-art methods on several benchmark datasets.