Do You Feel Like Passing Through Walls?: Effect of Self-Avatar Appearance on Facilitating Realistic Behavior in Virtual Environments

Abstract: Preventing users from walking through virtual boundaries (e.g., walls) is an important issue to be addressed in room-scale virtual environments (VEs), considering the safety and design limitations. Sensory feedback from wall collisions has been shown to be effective; however, it can disrupt the immersion. We assumed that a greater sense of presence would discourage users from walking through walls and conducted a two-factor between-subjects experiment (N = 92) that controls the anthropomorphism (realistic or abstract) and visibility (full-body or hand-only) of self-avatars. We analyzed the participants’ behaviors and the moment they first penetrated the wall in game-like VEs that gradually instigated participants to penetrate the walls. The results showed that the realistic full-body self-avatar was the most effective for discouraging the participants from penetrating the walls. Furthermore, the participants with lower presence tended to walk through the walls sooner. This study can contribute to applications that require realistic user responses in VEs.

25/04/2020

self-supervision, defense, attack, perceptual-features, classification, segmentation, object-detection, adversarial-learning, dynamic-defense, zero-shot

5:01

12/07/2020

virtual reality, vr, user studies, in-vr questionnaires, invrqs, break in presence, surveys, biosignals, research methods

9:46

18/07/2021

Samyak Datta, Oleksandr Maksymets, Judy Hoffman and
Stefan Lee, Dhruv Batra Georgia Tech &, Facebook AI Research, Devi Parikh Georgia Tech &, Facebook AI Research

Keywords Paper

5:08

25/04/2020

WalkingVibe: Reducing Virtual Reality Sickness and Improving Realism while Walking in VR using Unobtrusive Head-mounted Vibrotactile Feedback

Yiming Wang, Francesco Giuliari, Riccardo Berra and
Alberto Castellini, Alessio Del Bue, Alessandro Farinelli, Marco Cristani, Francesco Setti

adversarial robustness, adversarial defense, adversarial training, fast adversarial training, adversary-free training, adversarial attacks, efficient adversarial training, generalization, feature consistency, deep neural networks

1:01

22/09/2020

adversarial defense, feature denoise, multi-task learning, self-supervised learning, image restoration, lipschitz constant constraint, feature pyramid decoder

1:01

14/06/2020

model personalization, user adaptation, continual learning, domain adaptation, privacy, scalability, unsupervised learning

1:00

26/04/2020