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.

本研究首次调查了事件驱动视频帧插值模型在生成体育慢镜头视频方面的应用，通过设计和实现双摄像头录制系统，使用普通摄像头和事件驱动摄像头进行时间对齐和空间注册，并通过实验证明 TimeLens 等事件驱动视频帧插值模型能有效生成体育慢镜头视频，从而凸显了事件驱动摄像头在生成体育慢镜头内容方面的实际效用，为未来在该领域的研究努力奠定了基础。