Video Frame Interpolation (VFI) is important for video enhancement, frame
rate up-conversion, and slow-motion generation. The introduction of event
cameras, which capture per-pixel brightness changes asynchronously, has
significantly enhanced VFI capabilities, particularly for high-speed, nonlinear
motions. However, these event-based methods encounter challenges in low-light
conditions, notably trailing artifacts and signal latency, which hinder their
direct applicability and generalization. Addressing these issues, we propose a
novel per-scene optimization strategy tailored for low-light conditions. This
approach utilizes the internal statistics of a sequence to handle degraded
event data under low-light conditions, improving the generalizability to
different lighting and camera settings. To evaluate its robustness in low-light
condition, we further introduce EVFI-LL, a unique RGB+Event dataset captured
under low-light conditions. Our results demonstrate state-of-the-art
performance in low-light environments. Both the dataset and the source code
will be made publicly available upon publication. Project page:
this https URL

提出一种针对低光照条件下事件相机的视频帧插值的优化策略，利用场景的内部统计信息来处理低光照条件下退化的事件数据，提高对不同光照和相机设置的泛化能力。