PUBLICATIONS

2025

1. 

   Enhancing Smartphone Eye Tracking with Cursor-Based Interactive Implicit Calibration

   Chang Liu, Xiangyang Wang, Chun Yu, and 5 more authors

   In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, , 2025

   Abs DOI

   The limited accuracy of eye-tracking on smartphones restricts its use. Existing RGB-camera-based eye-tracking relies on extensive datasets, which could be enhanced by continuous fine-tuning using calibration data implicitly collected from the interaction. In this context, we propose COMETIC (Cursor Operation Mediated Eye-Tracking Implicit Calibration), which introduces a cursor-based interaction and utilizes the inherent correlation between cursor and eye movement. By filtering valid cursor coordinates as proxies for the ground truth of gaze and fine-tuning the eye-tracking model with corresponding images, COMETIC enhances accuracy during the interaction. Both filtering and fine-tuning use pre-trained models and could be facilitated using personalized, dynamically updated data. Results show COMETIC achieves an average eye-tracking error of 278.3 px (1.60 cm, 2.29°), representing a 27.2% improvement compared to that without fine-tuning. We found that filtering cursor points whose actual distance to gaze is 150.0 px (0.86 cm) yields the best eye-tracking results.

2. 

   AroMR: Decentralizing Olfactory Displays into the Environment for Olfactory-Augmented Experiences in Mixed Reality

   Yibo Wang, Ziqi Liu, Jiao Xue, and 1 more author

   In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, , 2025

   Abs DOI

   Smell, as a vital sensory modality, has the potential to significantly enhance user experience in immersive environments; however, its application in mixed reality (MR) remains underexplored. Current olfactory displays (ODs) are typically fixed to head-mounted displays (HMDs) as active olfactory inputs, limiting interaction and failing to capture the dynamic nature of human olfactory perception. To better integrate olfactory experiences into MR, we propose a "field-centric" strategy that decentralizes ODs into the physical environment as passive outputs. We further introduce AroMR, a proof-of-concept system that employs this strategy to incorporate olfactory feedback into MR experiences. A user study involving eight participants interacting with AroMR across three pre-designed scenarios revealed the potential of our approach to enhance engagement and create more immersive MR experiences. Based on these findings, we propose a conceptual design space to guide future research on integrating olfactory elements into MR.