Self-Distillation Based Multi-task Learning Model For Stylus Input Latency Compensation MHCI027
Year: 2025
Type: article
Abstract: Input latency significantly deteriorates the users experience during touchscreen interactions, especially when they engage in precision tasks such as writing or drawing with a stylus. We address this issue by first decomposing it into two constituent tasks: stylus nib future trajectory prediction and predicted trajectory length optimization, facilitating a more thorough investigation into balancing latency compensation and side-effects, and then proposing a novel multi-task learning architecture that integrates the consideration of both tasks, enhances overall performance through alternating-joint training. Additional usage of specific features generated with an active stylus and the adoption of a customized distance error metric are also aimed at accuracy improvement. Experiments reveal that the multi-task learning model gives 0.47, 1.30, and 2.24 pixels of average error in cases of prediction in 6, 14, and 20 ms, and offers better trade-offs between latency reduction and side-effects across a wide range of usage scenarios.
DOI: 10.1145/3743742
Authors: Kuangyu Liu, Fangjie Hou, Xinyi Zhang, Jianlin Li, Zuobin Ning, Xuecheng Wu, Shunli Mao
Institutions: Dow Chemical (China), Xiaomi (China)
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414161127