# EventGAN: Leveraging Large Scale Image Datasets for Event Cameras

arXiv: https://arxiv.org/abs/1912.01584 · HTML: https://mlsanity.com/paper/1912.01584/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 30 of 48)

- [Delivering Arbitrary-Modal Semantic Segmentation](https://mlsanity.com/paper/2303.01480/) (2023)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Masked Event Modeling: Self-Supervised Pretraining for Event Cameras](https://mlsanity.com/paper/2212.10368/) (2022)
- [A Voxel Graph CNN for Object Classification with Event Cameras](https://mlsanity.com/paper/2106.00216/) (2021)
- [Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.02618/) (2021)
- [EventCLIP: Adapting CLIP for Event-based Object Recognition](https://mlsanity.com/paper/2306.06354/) (2023)
- [Seeing Motion at Nighttime with an Event Camera](https://mlsanity.com/paper/2404.11884/) (2024)
- [CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation](https://mlsanity.com/paper/2307.15942/) (2023)
- [Efficient Human Pose Estimation via 3D Event Point Cloud](https://mlsanity.com/paper/2206.04511/) (2022)
- [V2CE: Video to Continuous Events Simulator](https://mlsanity.com/paper/2309.08891/) (2023)
- [LEOD: Label-Efficient Object Detection for Event Cameras](https://mlsanity.com/paper/2311.17286/) (2023)
- [Exploring Event-driven Dynamic Context for Accident Scene Segmentation](https://mlsanity.com/paper/2112.05006/) (2021)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Event-based Continuous Color Video Decompression from Single Frames](https://mlsanity.com/paper/2312.00113/) (2023)
- [Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning](https://mlsanity.com/paper/2303.12424/) (2023)
- [Motion-aware Latent Diffusion Models for Video Frame Interpolation](https://mlsanity.com/paper/2404.13534/) (2024)
- [Spiking Neural Networks for event-based action recognition: A new task to understand their advantage](https://mlsanity.com/paper/2209.14915/) (2022)
- [Towards Robust Event-based Networks for Nighttime via Unpaired Day-to-Night Event Translation](https://mlsanity.com/paper/2407.10703/) (2024)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Un-EVIMO: Unsupervised Event-Based Independent Motion Segmentation](https://mlsanity.com/paper/2312.00114/) (2023)
- [EvTexture: Event-driven Texture Enhancement for Video Super-Resolution](https://mlsanity.com/paper/2406.13457/) (2024)
- [Real-time event simulation with frame-based cameras](https://mlsanity.com/paper/2209.04634/) (2022)
- [Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras](https://mlsanity.com/paper/2404.01298/) (2024)
- [Event Quality Score (EQS): Assessing the Realism of Simulated Event Camera Streams via Distances in Latent Space](https://mlsanity.com/paper/2504.12515/) (2025)
- [How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection](https://mlsanity.com/paper/2506.13722/) (2025)
- [Exploring Event-based Human Pose Estimation with 3D Event Representations](https://mlsanity.com/paper/2311.04591/) (2023)
- [A PyTorch-Enabled Tool for Synthetic Event Camera Data Generation and Algorithm Development](https://mlsanity.com/paper/2503.09754/) (2025)
- [V2V: Scaling Event-Based Vision through Efficient Video-to-Voxel Simulation](https://mlsanity.com/paper/2505.16797/) (2025)
- [Learning to Remove Lens Flare in Event Camera](https://mlsanity.com/paper/2512.09016/) (2025)
- [Seeing the Unseen: Zooming in the Dark with Event Cameras](https://mlsanity.com/paper/2601.02206/) (2026)

## Abstract (arXiv)

Event cameras provide a number of benefits over traditional cameras, such as the ability to track incredibly fast motions, high dynamic range, and low power consumption. However, their application into computer vision problems, many of which are primarily dominated by deep learning solutions, has been limited by the lack of labeled training data for events. In this work, we propose a method which leverages the existing labeled data for images by simulating events from a pair of temporal image frames, using a convolutional neural network. We train this network on pairs of images and events, using an adversarial discriminator loss and a pair of cycle consistency losses. The cycle consistency losses utilize a pair of pre-trained self-supervised networks which perform optical flow estimation and image reconstruction from events, and constrain our network to generate events which result in accurate outputs from both of these networks. Trained fully end to end, our network learns a generative model for events from images without the need for accurate modeling of the motion in the scene, exhibited by modeling based methods, while also implicitly modeling event noise. Using this simulator, we train a pair of downstream networks on object detection and 2D human pose estimation from events, using simulated data from large scale image datasets, and demonstrate the networks' abilities to generalize to datasets with real events.