Event Generative Compression

Event cameras offer high temporal resolution and dynamic range. However, in dynamic scenes, they can generate event streams with data rates more than two orders of magnitude higher than conventional video, making transmission a major bottleneck for remote perception. While existing approaches primarily aim to preserve the event stream itself, we argue that when the downstream consumer only needs visual content, compression should instead preserve information useful for visual reconstruction and perception, rather than events themselves. We propose Event Generative Compression (EGC), which encodes each temporal window into a compact set of discrete symbols and uses a pretrained video diffusion model to recover the missing visual information. EGC learns an event-to-generation interface through latent alignment, discrete quantization, entropy coding, and generative adaptation, while reusing generated history for continuous reconstruction without transmitting reference frames. On DSEC and BS-ERGB, EGC achieves compression ratios of approximately 5,000–8,000 relative to losslessly coded event streams while retaining information for both temporally coherent video reconstruction and downstream tasks. The same low-rate messages support semantic segmentation, optical flow, and depth estimation, demonstrating the effectiveness of EGC in preserving useful visual information under ultra-low bitrate compression.