SynSacc: A Blender-to-V2E Pipeline for Synthetic Neuromorphic Eye-Movement Data and Sim-to-Real Spiking Model Training

TL;DR

This paper presents SynSacc, a pipeline that generates synthetic neuromorphic eye-movement data using Blender, and demonstrates its effectiveness for training spiking neural networks to classify eye movements accurately and efficiently.

Contribution

It introduces a novel Blender-based synthetic dataset for eye movements and evaluates SNNs trained on synthetic data for robust classification in real-world scenarios.

Findings

SNN models achieved up to 0.83 accuracy in classifying eye movements.

Synthetic data training improved robustness and computational efficiency.

Models maintained performance across different temporal resolutions.

Abstract

The study of eye movements, particularly saccades and fixations, are fundamental to understanding the mechanisms of human cognition and perception. Accurate classification of these movements requires sensing technologies capable of capturing rapid dynamics without distortion. Event cameras, also known as Dynamic Vision Sensors (DVS), provide asynchronous recordings of changes in light intensity, thereby eliminating motion blur inherent in conventional frame-based cameras and offering superior temporal resolution and data efficiency. In this study, we introduce a synthetic dataset generated with Blender to simulate saccades and fixations under controlled conditions. Leveraging Spiking Neural Networks (SNNs), we evaluate its robustness by training two architectures and finetuning on real event data. The proposed models achieve up to 0.83 accuracy and maintain consistent performance across varying temporal resolutions, demonstrating stability in eye movement classification. Moreover, the use of SNNs with synthetic event streams yields substantial computational efficiency gains over artificial neural network (ANN) counterparts, underscoring the utility of synthetic data augmentation in advancing event-based vision. All code and datasets associated with this work is available at https: //github.com/Ikhadija-5/SynSacc-Dataset.