EvDiff: High Quality Video with an Event Camera
Weilun Li, Lei Sun, Ruixi Gao, Qi Jiang, Yuqin Ma, Kaiwei Wang, Ming-Hsuan Yang, Luc Van Gool, Danda Pani Paudel

TL;DR
EvDiff is a novel event-based diffusion model that generates high-quality, colorful videos from monochromatic event streams, leveraging surrogate training to improve realism and fidelity without paired datasets.
Contribution
The paper introduces EvDiff, a diffusion-based framework with surrogate training that enhances video quality from event data and reduces computational costs.
Findings
Outperforms existing methods on real-world datasets
Generates high-quality colorful videos from monochrome event streams
Balances fidelity and realism effectively
Abstract
As neuromorphic sensors, event cameras asynchronously record changes in brightness as streams of sparse events with the advantages of high temporal resolution and high dynamic range. Reconstructing intensity images from events is a highly ill-posed task due to the inherent ambiguity of absolute brightness. Early methods generally follow an end-to-end regression paradigm, directly mapping events to intensity frames in a deterministic manner. While effective to some extent, these approaches often yield perceptually inferior results and struggle to scale up in model capacity and training data. In this work, we propose EvDiff, an event-based diffusion model that follows a surrogate training framework to produce high-quality videos. To reduce the heavy computational cost of high-frame-rate video generation, we design an event-based diffusion model that performs only a single forward…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · EEG and Brain-Computer Interfaces
