Difeng Yu | 俞迪枫

Difeng is a final (fourth) year Ph.D. student in Human-Computer Interaction Group, The University of Melbourne, advised by Dr. Jorge Goncalves (primary), Dr. Tilman Dingler, and Dr. Eduardo Velloso. He received his BSc degree in Computer Science from Xi’an Jiaotong-Liverpool University in 2018 and was a research assistant at X-CHI Lab directed by Dr. Hai-Ning Liang. He was a research intern at Meta Reality Labs in 2021. His research in Human-Computer Interaction (HCI) focuses on designing interactive techniques and modeling user behavior in augmented and virtual reality systems. He is also interested in computer graphics, sensing techniques, and machine learning.

Selected Publications

Optimizing the Timing of Intelligent Suggestion in Virtual Reality
D. Yu, R. Desai, T. Zhang, H. Benko, T. R. Jonker, A. Gupta (UIST '22) [PDF] [Video]
Intelligent suggestions based on target prediction models can enable low-friction input within VR and AR systems. For example, a system could highlight the predicted target and enable a user to select it with a simple click. However, as the probability estimates can be made at any time, it is unclear when an intelligent suggestion should be presented. Earlier suggestions could save a user time and effort but be less accurate, while later ones could be more accurate but save less time and effort. We thus propose a computational framework for determining the optimal timing of intelligent suggestions.
Blending On-Body and Mid-Air Interaction in Virtual Reality
D. Yu, Q. Zhou, T. Dingler, E. Velloso, J. Goncalves (ISMAR '22) [PDF] [Video]
On-body interfaces, which leverage the human body's surface as an input or output platform, can provide new opportunities for designing VR interaction. However, it is unclear how they can best support current VR systems that mainly rely on mid-air interaction. We propose BodyOn, a collection of six design patterns that leverage combined on-body and mid-air interfaces to achieve more effective 3D interaction. A user may use thumb-on-finger gestures, finger-on-arm gestures, or on-body displays with mid-air input, including hand movement and orientation, to complete a VR task.
Gaze-Supported 3D Object Manipulation in Virtual Reality
D. Yu, X. Lu, R. Shi, H. N. Liang, T. Dingler, E. Velloso, J. Goncalves (CHI '21) [PDF] [Video]
This work investigates integration, coordination, and transition strategies of gaze and hand input for 3D object manipulation in VR. Specifically, we aim to understand whether incorporating gaze input can benefit VR object manipulation tasks and how it should be combined with hand input for improved usability and efficiency. We designed and compared four techniques that leverage different combination strategies. For example, ImplicitGaze allows the transition between gaze and hand input to happen without any trigger mechanism like button pressing.
Fully-Occluded Target Selection in Virtual Reality
D. Yu, Q. Zhou, J. Newn, T. Dingler, E. Velloso, J. Goncalves (TVCG '20) [PDF] [Video]
🏅 Best Paper Nominee at ISMAR 2020
The presence of fully-occluded targets is common within virtual environments, ranging from a virtual object located behind a wall to a datapoint of interest hidden in a complex visualization. However, efficient input techniques for locating and selecting these targets are mostly underexplored in VR systems. In this research, we developed ten techniques for fully-occluded target selection in VR and evaluated their performance through two user studies. We further demostrated how the techniques can be applied to real application scenarios.
Modeling Endpoint Distribution of Pointing Selection Tasks in Virtual Reality Environments
D. Yu, H. N. Liang, X. Lu, K. Fan, B. Ens (TOG '19) [PDF] [Video]
Understanding the endpoint distribution of pointing selection tasks can reveal underlying patterns on how users tend to acquire a target. We introduce EDModel, a novel endpoint distribution model which can predict how endpoint distributes when selecting targets with different characters (width, distance, and depth) in virtual reality (VR) environments. We demonstrate three applications of EDModel and opensource our experiment data for future research purposes.
Design and Evaluation of Visualization Techniques of Off-Screen and Occluded Targets in Virtual Reality Environments
D. Yu, H. N. Liang, K. Fan, H. Zhang, C. Fleming, K. Papangelis (TVCG '19) [PDF]
Locating targets of interest in a 3D environment often becomes difficult when the targets reside outside the user’s view or are occluded by other objects (e.g. buildings) in the environment. In this research, we explored the design and evaluation of five visualization techniques (we call 3DWedge, 3DArrow, 3DMinimap, Radar, and 3DWedge+). Based on the results of the two user studies, we provide a set of recommendations for the design of visualization techniques of off-screen and occluded targets in 3D VE.
PizzaText: Text Entry for Virtual Reality Systems Using Dual Thumbsticks
D. Yu, K. Fan, H. Zhang, D. V. Monteiro, W. Xu, and H. N. Liang (TVCG '18) [PDF] [Video]
PizzaText is a circular keyboard layout technique for text entry in virtual reality systems that uses the dual thumbsticks of a hand-held game controller. By rotating the two joysticks of a game controller, users can easily enter text by using this circular keyboard layout. This technique makes text entry simple, easy, and efficient, even for novice users. The results show that novice users can achieve an average of 8.59 Words per Minute, while expert users are able to reach 15.85 WPM, with just two hours of training.
View full publications

Side Projects

EmoDrone VR Haptic Drone [Video]
ShadowDancXR ShadowDancXR [Video]
VRHome RL for StarCraft 2 [Video]
On-Pet Interaction On-Pet Interaction [Report]
VRHome VRHome [Video]
CopyQues CopyQues [GitHub]
RestReminder Rest Reminder