In this work we study the use of 3D hand poses to recognize first-person dynamic hand
actions interacting with 3D objects. Towards this goal, we collected RGB-D video sequences …

In this article, we propose a transformer-based RGB-D egocentric action recognition
framework, called Trear. It consists of two modules: 1) interframe attention encoder and 2) …

This paper proposes a new neural network based on SPD manifold learning for skeleton-
based hand gesture recognition. Given the stream of hand's joint positions, our approach …

We propose a framework for recognizing human actions from skeleton data by modeling the
underlying dynamic process that generates the motion pattern. We capture three major …

In this work, we face the problem of unsupervised domain adaptation with a novel deep
learning approach which leverages on our finding that entropy minimization is induced by …

Point clouds contain rich spatial information, which provides complementary cues for
gesture recognition. In this paper, we formulate gesture recognition as an irregular …

Pose-based hand gesture recognition has been widely studied in the recent years.
Compared with full body action recognition, hand gesture involves joints that are more …

Skeleton-based gesture recognition is very challenging, as the high-level information in
gesture is expressed by a sequence of complexly composite motions. Previous works often …

In this paper, we propose a novel method for representation and classification of two-person
interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian …

Spectral graph convolutional networks (GCNs) are particular deep models which aim at
extending neural networks to arbitrary irregular domains. The principle of these networks …