Slow-motion replays provide a thrilling perspective on pivotal moments within sports games, offering a fresh and captivating visual experience. However, capturing slow-motion footage typically demands high-tech, expensive cameras and infrastructures. Deep learning Video Frame Interpolation (VFI) techniques have emerged as a promising avenue, capable of generating high-speed footage from regular camera feeds. Moreover, the utilization of event-based cameras has recently gathered attention as they provide valuable motion information between frames, further enhancing the VFI performances. In this work, we present a first investigation of event-based VFI models for generating sports slow-motion videos. Particularly, we design and implement a bi-camera recording setup, including an RGB and an event-based camera to capture sports videos, to temporally align and spatially register both cameras. Our experimental validation demonstrates that TimeLens, an off-the-shelf event-based VFI model, can effectively generate slow-motion footage for sports videos. This first investigation underscores the practical utility of event-based cameras in producing sports slow-motion content and lays the groundwork for future research endeavors in this domain.

翻译：慢动作回放为体育赛事中的关键时刻提供了激动人心的视角，带来新颖且引人入胜的视觉体验。然而，捕捉慢动作画面通常需要高科技、昂贵的相机及配套基础设施。深度学习视频帧插值技术已成为一条前景广阔的途径，能够从常规相机拍摄的画面中生成高速影像。此外，事件相机的应用近期备受关注，因其能够提供帧间有价值的运动信息，从而进一步提升视频帧插值的性能。本研究首次探索了基于事件相机的视频帧插值模型在生成体育慢动作视频中的应用。具体而言，我们设计并搭建了一套双相机录制系统，包含一台RGB相机和一台事件相机，用于同步采集体育视频，并对两台相机进行时间对齐与空间配准。实验验证表明，现成的基于事件相机的视频帧插值模型TimeLens能够有效生成体育视频的慢动作画面。这项初步研究证实了事件相机在制作体育慢动作内容方面的实用价值，并为该领域未来的研究工作奠定了基础。