Abstract
We introduce CapStARE, a capsule-based spatio-temporal architecture for gaze estimation that integrates a ConvNeXt backbone, capsule formation with attention routing, and dual GRU decoders specialized for slow and rapid gaze dynamics. This modular design enables efficient part-whole reasoning and disentangled temporal modeling, achieving state-of-the-art performance on ETH-XGaze (3.36) and MPIIFaceGaze (2.65) while maintaining real-time inference (< 10 ms). The model also generalizes well to unconstrained conditions in Gaze360 (9.06) and human-robot interaction scenarios in RT-GENE (4.76), outperforming or matching existing methods with fewer parameters and greater interpretability. These results demonstrate that CapStARE offers a practical and robust solution for real-time gaze estimation in interactive systems. The related code and results for this article can be found on: this https URL
Abstract (translated)
我们介绍了CapStARE，这是一种基于胶囊的时空架构，用于眼动追踪。该架构结合了ConvNeXt骨干网络、带有注意力路由的胶囊生成以及专门针对慢速和快速眼球动态的双GRU解码器。这种模块化设计使得有效的部分-整体推理和时间模型分离成为可能，在ETH-XGaze（3.36）和MPIIFaceGaze（2.65）数据集上实现了最先进的性能，同时保持了实时推断速度（< 10 毫秒）。该模型在不受约束条件下的Gaze360（9.06）和人机交互场景中的RT-GENE（4.76）中也具有良好的泛化能力，并且通过使用更少的参数和更高的可解释性优于或匹敌现有的方法。这些结果表明，CapStARE为互动系统中的实时眼动追踪提供了一个实用而稳健的解决方案。与此文章相关的代码和结果可以在以下链接找到：[此URL]
URL
https://arxiv.org/abs/2509.19936
