v2e: Video-to-Events Conversion Toolbox

• v2e is a video-to-events conversion toolbox that synthesizes dynamic vision sensor (DVS) events with realistic non-ideal behaviors such as noise and threshold mismatches.
• It employs a conversion model that maps frame intensities to logarithmic space, applies temporal differencing, and generates asynchronous event streams via thresholding.
• v2e is widely used for generating synthetic datasets in tasks like object recognition, driving, eye tracking, and robotics to bridge the sim-to-real gap.

Searching arXiv for recent and foundational papers on "v2e" in event-based vision to ground the article. v2e most commonly denotes a toolbox for generating realistic synthetic Dynamic Vision Sensor (DVS) events from conventional video frames, introduced to help meet the increasing need for DVS event camera data and to model non-ideal sensor behavior rather than only idealized event generation (Hu et al., 2020). Within event-based vision, it is used to convert rendered or recorded frame sequences into asynchronous event streams for training and evaluating models in object recognition, driving, eye tracking, hand detection, pedestrian analysis, and robotics (Hu et al., 2020). The same string also appears in unrelated literatures, including 5G Vehicle-to-Everything systems, vehicle-to-edge mapping in the Internet of Vehicles, smart-contract vulnerability validation, and the double-energy double-velocity method in fission studies, so the intended meaning is strongly context-dependent (Jover, 2019, Talpur et al., 2022, Zhang et al., 15 Apr 2026, Jansson et al., 2017).

1. Definition in event-based vision

In the event-based vision literature, v2e is a video-to-events conversion toolbox. Its central purpose is to synthesize DVS-like output from intensity frames while retaining properties that matter for downstream learning, especially asynchronous polarity events, brightness-dependent response, and sensor noise processes (Hu et al., 2020). This role is operationally important because real event datasets remain limited in many domains; several later works explicitly use v2e to construct task-specific datasets where native DVS recordings are unavailable or insufficient, including eye tracking from LPW videos, JAAD-derived pedestrian data, Egohands-derived multimodal hand data, Blender-generated microsaccades, and ROS/Gazebo robotic simulation (Chen et al., 2023, Sakhai et al., 4 Sep 2025, Kota et al., 9 Jun 2026, Shariff et al., 28 Oct 2025, Vinod et al., 25 Aug 2025).

The event representation used by v2e-based pipelines follows the standard asynchronous tuple form

$e_k = (u_k, v_k, t_k, p_k),$

where pixel coordinates, timestamps, and polarity are emitted when brightness changes cross positive or negative thresholds (Iddrisu et al., 9 Feb 2026). In that sense, v2e is not merely a frame differencer. It is a synthetic sensor model intended to approximate what a real DVS would output under the same visual stimulus, including departures from ideal behavior (Hu et al., 2020).

2. Conversion model and event formation

The conversion process used in v2e-centered pipelines typically begins by mapping rendered or recorded frames to a brightness representation and then to log intensity. A common formulation is

$\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$

followed by temporal differencing,

$$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$

An ON event is generated if the change exceeds $\theta_{on}$, and an OFF event is generated if the change is less than $-\theta_{off}$; multiple events may be generated per time step if the increment is large (Iddrisu et al., 9 Feb 2026).

The original v2e methodology adds several stages around that basic thresholding rule. RGB frames are converted to luma following ITU-R BT.709; spatial resizing can be applied; temporal resolution can be increased through a deep Super-SloMo network trained specifically on luma frames; and the luma signal is mapped to log scale, with low values mapped linearly to reduce quantization noise (Hu et al., 2020). Real DVS pixels are then modeled as having finite temporal bandwidth, expressed through a lowpass IIR filter on log intensity,

$$L_{\rm lp}[t+1] = L_{\rm lp}[t] + \alpha(Y)\cdot(L[t+1]-L_{\rm lp}[t]),$$

where $\alpha(Y)$ is proportional to local luma, making dark pixels react more slowly (Hu et al., 2020).

Event multiplicity is also modeled explicitly. When the lowpass log-intensity change exceeds threshold, the number of emitted events is computed as

$$N_{\rm e} = \left\lfloor \frac{\Delta L}{\theta} \right\rfloor,$$

with separate positive and negative thresholds $\theta_{\rm ON}>0$ and $\theta_{\rm OFF}<0$, typically $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$0 (Hu et al., 2020). This makes the output closer to real asynchronous DVS behavior than a single-event-per-frame approximation.

3. Realism, non-idealities, and sensor misconceptions

A defining characteristic of v2e is that it models non-ideal DVS behavior rather than assuming a noiseless and infinitely fast sensor. The toolbox includes pixel-level Gaussian event threshold mismatch, finite intensity-dependent bandwidth, and intensity-dependent noise (Hu et al., 2020). Threshold mismatch is represented by sampling each pixel threshold from

$\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$1

with typical $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$2, while hot pixels, leak events, and temporal shot noise are also modeled (Hu et al., 2020).

These non-idealities are not incidental. The original work explicitly used v2e to clarify incorrect claims about DVS motion blur and latency characteristics in recent literature. The paper argues that the claim that event cameras have “no motion blur” is incorrect because finite pixel bandwidth produces a form of motion blur, especially under very low light when bandwidth drops and events are stretched over longer periods (Hu et al., 2020). Likewise, the claim that DVS latency is always in the microsecond regime is rejected; under low illumination, response slows because of low photocurrent, and real-world latencies are described as several milliseconds rather than always microseconds (Hu et al., 2020).

Later synthetic-data studies use these realism mechanisms as a control knob. SynSacc notes that V2E thresholds are empirically determined to match DVS noise and event rates, and that modeled noise can mitigate the domain gap between synthetic and real data (Iddrisu et al., 9 Feb 2026). EventEgoHands varies v2e parameters to create clean, noisy, and mixed event datasets, with separate settings for threshold mismatch, shot noise, leak events, and cutoff frequency (Kota et al., 9 Jun 2026). DVS-PedX similarly logs sequence-specific v2e arguments and uses ON/OFF polarity generation, interpolation when useful, and event accumulation into 33 ms frames aligned with the source video rate (Sakhai et al., 4 Sep 2025).

4. Synthetic dataset construction and sim-to-real training

v2e is often embedded in a broader synthetic-data pipeline rather than used in isolation. In Blender-centered setups, 3D scenes and eyeball or face models are created, gaze vectors are animated to simulate saccades, fixations, smooth pursuits, or microsaccades, and the resulting image sequences are rendered at high frame rates before conversion to events by V2E (Iddrisu et al., 9 Feb 2026). Validation in such pipelines is performed by comparing event rates, spatial and temporal distributions, and motion traces against real event datasets or empirical eye-movement statistics; qualitative validation looks for event bursts during saccades and sparse firing during fixations (Iddrisu et al., 9 Feb 2026). This suggests that v2e is routinely treated as the conversion stage in a synthetic-to-neuromorphic workflow rather than as the full simulation stack.

The eye-tracking literature illustrates this use with unusually concrete preprocessing choices. In "3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network" (Chen et al., 2023), LPW videos at $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$3 are resized to $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$4, converted to events with v2e, binned into voxel-grid frames with $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$5 ms to match label timing, and then resized to $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$6 for training efficiency; only one-third of the 66 LPW videos are selected, specifically those with above-average eye movement speed, yielding approximately 11,000 event frames stored as .h5 files (Chen et al., 2023). Temporal augmentation is then performed by sliding-window sampling with stride 1 (Chen et al., 2023).

A related but more explicitly synthetic pipeline is "SynSacc: A Blender-to-V2E Pipeline for Synthetic Neuromorphic Eye-Movement Data and Sim-to-Real Spiking Model Training" (Iddrisu et al., 9 Feb 2026). There, two SNN architectures are trained on Blender-generated event data and finetuned on real event data; the models achieve up to 0.83 accuracy and maintain consistent performance across varying temporal resolutions, while the use of SNNs with synthetic event streams yields substantial computational efficiency gains over ANN counterparts (Iddrisu et al., 9 Feb 2026). The microsaccade benchmark extends the same logic to sub-millisecond motion. Seven Blender-rendered classes spanning angular displacements from 0.5 to 2.0 degrees are converted to event streams using v2e, with durations ranging from 0.25 ms to 2.25 ms; Spiking-VGG11, Spiking-VGG13, Spiking-VGG16, and Spiking-VGG16Flow achieve around 90 percent average accuracy, and the benchmark is explicitly constructed so that classification is independent of event count or duration (Shariff et al., 28 Oct 2025).

5. Empirical uses across perception and robotics

The original v2e paper demonstrated two canonical use cases. For object recognition on N-Caltech 101, training only on synthetic events generated under multiple lighting conditions yielded 83.36% test accuracy on real events, training only on real events yielded 86.74%, and pretraining on v2e synthetic events followed by finetuning on real events yielded 87.85% (Hu et al., 2020). For night driving, a car detector trained with v2e events showed an average accuracy improvement of 40% compared to YOLOv3 trained on intensity frames (Hu et al., 2020). These results are repeatedly cited as evidence that synthetic event pretraining can narrow the sim-to-real gap when lighting variation and sensor non-idealities are represented.

Later task-specific datasets follow the same pattern. DVS-PedX converts all 346 JAAD RGB clips, each 5–10 s at 30 Hz, into event streams with v2e, using a contrast threshold of approximately 0.3, ON/OFF polarity generation, and 33 ms accumulations aligned with the original frame rate; each sequence provides event frames, AVI visualizations, AEDAT 2.0 and AEDAT 4.0 raw files, and metadata, and baseline SNNs reveal a sim-to-real gap between synthetic CARLA data and JAAD-derived v2e data (Sakhai et al., 4 Sep 2025). EventEgoHands uses v2e to synthesize six versions of an egocentric hand dataset from Egohands, varying interpolation factor, scale factor, and event model. The best result is obtained by the mixed version $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$7, which reaches $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$8, $\mathcal{L}_t(u,v) = \log(I_t(u,v) + \epsilon),$9, and $$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$0; the paper attributes this to combining clean and noisy synthetic events for improved robustness (Kota et al., 9 Jun 2026).

In robotics, "SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation" (Vinod et al., 25 Aug 2025) integrates v2e into a ROS2/Gazebo package that subscribes to /camera/image_raw, converts frames to grayscale, forwards them to a v2ecore EventEmulator, and publishes synthetic DVS events to /dvs/events. The package is used for object following with a mobile robot and object detection plus grasping with a UR5 manipulator. In navigation, RGB+Event fusion yields centroid error $$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$1 px and success rate 93.3%, compared with $$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$2 px and 86.7% for RGB-only and $$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$3 px and 73.3% for Event-only (Vinod et al., 25 Aug 2025). In manipulation, RGB+Event yields error $$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$4 mm, accuracy 71.4%, latency $$\Delta \mathcal{L}_{t}(u,v) = \mathcal{L}_t(u,v) - \mathcal{L}_{t-1}(u,v).$$5 ms, and success rate 51.7%, outperforming RGB-only and Event-only on the reported setup (Vinod et al., 25 Aug 2025). A plausible implication is that v2e is increasingly used not only for offline dataset conversion but also for closed-loop policy learning in simulators.

6. Acronym overlap outside event-camera research

The string “v2e” is not unique to event-based vision. In the supplied arXiv literature it appears in several unrelated technical senses.

Usage	Field	Description
Vehicle-to-Everything (V2E)	5G / vehicular communications	5G is described as a cornerstone for Vehicle-to-Vehicle and Vehicle-to-Everything systems, with strict communication and security goals (Jover, 2019).
Vehicle-to-Edge (V2E) mapping	Internet of Vehicles	V2E denotes mapping vehicles to edge nodes for low-delay, attack-resilient service placement and load balancing (Talpur et al., 2022).
V2E (Validate-to-Exploit)	Smart-contract security	V2E is a framework that validates whether a reported smart-contract vulnerability is truly exploitable through PoC generation, validation, and refinement (Zhang et al., 15 Apr 2026).
2E-2v method (“v2e”)	Nuclear fission instrumentation	The double-energy double-velocity method is discussed as “2E-2v (v2e)” in analyses of fission-fragment mass and prompt-neutron correlations (Jansson et al., 2017).

Because these meanings are all current in arXiv-indexed research, standalone uses of “v2e” are potentially ambiguous. In computer vision and neuromorphic sensing, however, the default referent is typically the video-to-events toolbox introduced in "v2e: From Video Frames to Realistic DVS Events" (Hu et al., 2020).