Reconstructing 3D digital models of humans from sensory data is a long-standing problem
in computer vision and graphics with a variety of applications in VR/AR, film production, and …

From the past few decades, Human activity recognition (HAR) is one of the vital research
areas in computer vision in which much research is ongoing. The researcher's focus is …

Recently, numerous methods have been introduced for three-dimensional (3-D) action
recognition using handcrafted feature descriptors coupled traditional classifiers. However …

In this paper, we propose a novel space-time geometric representation of human landmark
configurations and derive tools for comparison and classification. We model the temporal …

Machine Learning play one of the vital role in current technological world. Metric Training or
learning is a key problem in machine learning techniques. So far nonlinear metric learning …

Artificial Intelligence (AI) systems are increasingly pervasive: Internet of Things, in-car
intelligent devices, robots, and virtual assistants, and their large-scale adoption makes it …

In this work we propose a novel solution for 3D skeleton-based human motion prediction.
The objective of this task consists in forecasting future human poses based on a prior …

Person re-identification is typically performed using 2D still images or videos, where
photometric appearance is the main visual cue used to discover the presence of a target …

In this paper, we investigate unsupervised representation learning for skeleton action
recognition, and develop a simple yet effective framework: SKeletal Twins (SKT), which is …

Human motion prediction aims to forecast future human poses given a prior pose sequence.
The discontinuity of the predicted motion and the performance deterioration in long-term …