An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display

TL;DR

This paper introduces an immersive telepresence system using RGB-D sensors and HMDs that enhances remote interaction through body gestures, with applications in education and entertainment, emphasizing system design, implementation, and performance.

Contribution

The paper presents a novel telepresence system integrating egocentric pose recognition with RGB-D sensors and HMDs, enabling intuitive interaction and manipulation of virtual objects.

Findings

System runs comfortably and reliably in various scenarios

Demonstrates effective gesture-based interaction for education

Provides promising preliminary results for cyberlearning applications

Abstract

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to ef- fectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The highly interactive and flexible environment can capture and sustain more student attention than the traditional classroom setting and, thus, delivers a compelling experience to the students. Our main focus here is to investigate possible solutions for the system design and implementation and devise strategies for fast, efficient computation suitable for visual data processing and network transmission. We describe the technique and experiments in details and provide quantitative performance results, demonstrating our system can be run comfortably and reliably for different application scenarios. Our preliminary results are promising and demonstrate the potential for more compelling directions in cyberlearning.