# Exploring Temporal Dynamics in Event-based Eye Tracker

arXiv: https://arxiv.org/abs/2503.23725 · HTML: https://mlsanity.com/paper/2503.23725/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2303.17472/) (2023)
- [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021)
- [A Voxel Graph CNN for Object Classification with Event Cameras](https://mlsanity.com/paper/2106.00216/) (2021)
- [3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network](https://mlsanity.com/paper/2308.11771/) (2023)
- [SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition](https://mlsanity.com/paper/2310.07189/) (2023)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [Gaze-Contingent Ocular Parallax Rendering for Virtual Reality](https://mlsanity.com/paper/1906.09740/) (2019)
- [Event-Based Eye Tracking. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.11770/) (2024)
- [Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba](https://mlsanity.com/paper/2405.06116/) (2024)
- [A Simple and Effective Point-based Network for Event Camera 6-DOFs Pose Relocalization](https://mlsanity.com/paper/2403.19412/) (2024)
- [A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera](https://mlsanity.com/paper/2404.08858/) (2024)
- [A Frequency Domain Neural Network for Fast Image Super-resolution](https://mlsanity.com/paper/1712.03037/) (2017)
- [Bina-Rep Event Frames: a Simple and Effective Representation for Event-based cameras](https://mlsanity.com/paper/2202.13662/) (2022)
- [MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye tracking](https://mlsanity.com/paper/2404.12083/) (2024)
- [FACET: Fast and Accurate Event-Based Eye Tracking Using Ellipse Modeling for Extended Reality](https://mlsanity.com/paper/2409.15584/) (2024)
- [FAPNet: An Effective Frequency Adaptive Point-based Eye Tracker](https://mlsanity.com/paper/2406.03177/) (2024)

## Built on by (most cited first, 6 of 6)

- [Event-Based Eye Tracking. 2025 Event-based Vision Workshop](https://mlsanity.com/paper/2504.18249/) (2025)
- [evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition](https://mlsanity.com/paper/2508.03609/) (2025)
- [Neuromorphic Eye Tracking for Low-Latency Pupil Detection](https://mlsanity.com/paper/2512.09969/) (2025)
- [Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modeling](https://mlsanity.com/paper/2603.14077/) (2026)
- [Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach](https://mlsanity.com/paper/2604.26285/) (2026)
- [DIMOS: Disentangling Instance-level Moving Object Segmentation](https://mlsanity.com/paper/2606.12826/) (2026)

## Abstract (arXiv)

Eye-tracking is a vital technology for human-computer interaction, especially in wearable devices such as AR, VR, and XR. The realization of high-speed and high-precision eye-tracking using frame-based image sensors is constrained by their limited temporal resolution, which impairs the accurate capture of rapid ocular dynamics, such as saccades and blinks. Event cameras, inspired by biological vision systems, are capable of perceiving eye movements with extremely low power consumption and ultra-high temporal resolution. This makes them a promising solution for achieving high-speed, high-precision tracking with rich temporal dynamics. In this paper, we propose TDTracker, an effective eye-tracking framework that captures rapid eye movements by thoroughly modeling temporal dynamics from both implicit and explicit perspectives. TDTracker utilizes 3D convolutional neural networks to capture implicit short-term temporal dynamics and employs a cascaded structure consisting of a Frequency-aware Module, GRU, and Mamba to extract explicit long-term temporal dynamics. Ultimately, a prediction heatmap is used for eye coordinate regression. Experimental results demonstrate that TDTracker achieves state-of-the-art (SOTA) performance on the synthetic SEET dataset and secured Third place in the CVPR event-based eye-tracking challenge 2025. Our code is available at https://github.com/rhwxmx/TDTracker.