Recently, event cameras have shown large applicability in several computer vision fields
especially concerning tasks that require high temporal resolution. In this work, we …

Monocular egocentric 3D human motion capture is a challenging and actively researched
problem. Existing methods use synchronously operating visual sensors (eg RGB cameras) …

Deep models trained using synthetic data require domain adaptation to bridge the gap
between the simulation and target environments. State-of-the-art domain adaptation …

Abstract Human Pose Estimation (HPE) is crucial as a building block for tasks that are based
on the accurate understanding of human position, pose and movements. Therefore …

3D hand tracking from a monocular video is a very challenging problem due to hand
interactions, occlusions, left-right hand ambiguity, and fast motion. Most existing methods …

Comprehensive capturing of human motions requires both accurate captures of complex
poses and precise localization of the human within scenes. Most of the HPE datasets and …

Motion segmentation is a formidable computer vision task, aiming to segment moving targets
from a dynamic scene. In this paper, we choose to introduce an additional modality to bolster …

Event cameras are bio-inspired dynamic vision sensors that are superior to frame-based
cameras in terms of low power consumption, high dynamic range, and high temporal …

Capturing the 3D human body is one of the important tasks in computer vision with a wide
range of applications such as virtual reality and sports analysis. However conventional …

Fast and accurate auto-focus in adverse conditions remains an arduous task. The
emergence of event cameras has opened up new possibilities for addressing the challenge …