UniE2F: A Unified Diffusion Framework for Event-to-Frame Reconstruction with Video Foundation Models

ArXi:2602.19202v2 Announce Type: replace Event cameras excel at high-speed, low-power, and high-dynamic-range scene perception. However, as they fundamentally record only relative intensity changes rather than absolute intensity, the resulting data streams suffer from a significant loss of spatial information and static texture details. In this paper, we address this limitation by leveraging the generative prior of a pre-trained video diffusion model to reconstruct high-fidelity video frames from sparse event data.