# V2CE: Video to Continuous Events Simulator

arXiv: https://arxiv.org/abs/2309.08891 · HTML: https://mlsanity.com/paper/2309.08891/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://mlsanity.com/paper/2308.14419/) (2023)
- [EventGAN: Leveraging Large Scale Image Datasets for Event Cameras](https://mlsanity.com/paper/1912.01584/) (2019)
- [Single Image Optical Flow Estimation with an Event Camera](https://mlsanity.com/paper/2004.00347/) (2020)
- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [Low-Light Video Enhancement with Synthetic Event Guidance](https://mlsanity.com/paper/2208.11014/) (2022)
- [Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks](https://mlsanity.com/paper/2104.12579/) (2021)
- [Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment](https://mlsanity.com/paper/2301.06648/) (2023)
- [Dynamic Vision Sensors for Human Activity Recognition](https://mlsanity.com/paper/1803.04667/) (2018)
- [Tracking 6-DoF Object Motion from Events and Frames](https://mlsanity.com/paper/2103.15568/) (2021)

## Built on by (most cited first, 25 of 25)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Motion-aware Latent Diffusion Models for Video Frame Interpolation](https://mlsanity.com/paper/2404.13534/) (2024)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in the Video-to-Events Simulator](https://mlsanity.com/paper/2411.12250/) (2024)
- [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)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [GS2E: Gaussian Splatting is an Effective Data Generator for Event Stream Generation](https://mlsanity.com/paper/2505.15287/) (2025)
- [V2V: Scaling Event-Based Vision through Efficient Video-to-Voxel Simulation](https://mlsanity.com/paper/2505.16797/) (2025)
- [Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis](https://mlsanity.com/paper/2508.11988/) (2025)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input](https://mlsanity.com/paper/2406.03439/) (2024)
- [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://mlsanity.com/paper/2502.18490/) (2025)
- [EventTracer: Fast Path Tracing-based Event Stream Rendering](https://mlsanity.com/paper/2508.18071/) (2025)
- [Learning to Remove Lens Flare in Event Camera](https://mlsanity.com/paper/2512.09016/) (2025)
- [Event-VLA: Action-Conditioned Event Fusion for Robust Vision-Language-Action Model](https://mlsanity.com/paper/2606.29384/) (2026)
- [Raw2Event: Converting Raw Frame Camera into Event Camera](https://mlsanity.com/paper/2509.06767/) (2025)
- [FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics](https://mlsanity.com/paper/2606.26636/) (2026)
- [Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation](https://mlsanity.com/paper/2511.18037/) (2025)
- [Generative Anonymization in Event Streams](https://mlsanity.com/paper/2604.12803/) (2026)
- [TIDES: Time-Derivative Event Simulation via Deformable Reconstruction](https://mlsanity.com/paper/2606.02058/) (2026)
- [FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness](https://mlsanity.com/paper/2606.06856/) (2026)
- [EVIS: Real-Time Event Camera Simulation with Multimodal Supervision in NVIDIA Isaac Sim](https://mlsanity.com/paper/2607.08098/) (2026)
- [Event ActivityNet: A Large-Scale Simulated-Event Benchmark for Untrimmed Action Understanding](https://mlsanity.com/paper/2608.01948/) (2026)
- [Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition](https://mlsanity.com/paper/2608.30792/) (2026)
- [Event Cameras for Melt-Pool Monitoring in Additive Manufacturing: A Benchmark and a Cross-Machine Transfer Analysis](https://mlsanity.com/paper/2610.06973/) (2026)

## Abstract (arXiv)

Dynamic Vision Sensor (DVS)-based solutions have recently garnered significant interest across various computer vision tasks, offering notable benefits in terms of dynamic range, temporal resolution, and inference speed. However, as a relatively nascent vision sensor compared to Active Pixel Sensor (APS) devices such as RGB cameras, DVS suffers from a dearth of ample labeled datasets. Prior efforts to convert APS data into events often grapple with issues such as a considerable domain shift from real events, the absence of quantified validation, and layering problems within the time axis. In this paper, we present a novel method for video-to-events stream conversion from multiple perspectives, considering the specific characteristics of DVS. A series of carefully designed losses helps enhance the quality of generated event voxels significantly. We also propose a novel local dynamic-aware timestamp inference strategy to accurately recover event timestamps from event voxels in a continuous fashion and eliminate the temporal layering problem. Results from rigorous validation through quantified metrics at all stages of the pipeline establish our method unquestionably as the current state-of-the-art (SOTA).