What is it about?
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.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.
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Why is it important?
Most researchers have focused solely on accurate prediction or have exerted very little effort in predicted trajectory length (i.e., number of future positions to predict) control while designing latency compensation approaches. By contrast, this paper views these two tasks as interrelated components of a unified mechanism. We contribute a novel prediction framework for touch devices that leverages multi-task learning to achieve better trade-offs between latency reduction and potential side effects.
Perspectives
This article addresses a highly prevalent and noteworthy issue in the field. It adopts an innovative angle by integrating multiple challenges within the domain and proposes an effective solution leveraging artificial intelligence methodologies. The approach demonstrates both theoretical depth and practical applicability, making it a compelling reference for researchers and practitioners alike. Its contribution to reducing stylus input latency while minimizing error-related side effects represents a significant advancement, worthy of attention from the academic and industrial communities.
Kuangyu Liu
Xiaomi
Read the Original
This page is a summary of: Self-Distillation Based Multi-task Learning Model For Stylus Input Latency Compensation MHCI027, Proceedings of the ACM on Human-Computer Interaction, September 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3743742.
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