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: https://naturezhanghn.github.io/sim2real.

翻译：视频帧插值（VFI）在视频增强、帧率上转换和慢动作生成中具有重要意义。事件相机通过异步捕获像素级亮度变化，显著提升了VFI的能力，特别是在处理高速非线性运动方面。然而，这些基于事件的方法在低光照条件下面临挑战，尤其是拖尾伪影和信号延迟问题，这阻碍了其直接应用和泛化能力。针对这些问题，我们提出了一种专为低光照条件设计的新型逐场景优化策略。该方法利用序列的内部统计特性来处理低光照下退化的事件数据，从而提升对不同光照和相机设置的泛化能力。为了评估其在低光照条件下的鲁棒性，我们进一步引入了EVFI-LL，这是一个在低光照条件下捕获的独特RGB+事件数据集。我们的实验结果表明，该方法在低光照环境下达到了最先进的性能。数据集和源代码将在论文发表后公开。项目页面：https://naturezhanghn.github.io/sim2real。