This paper introduces a LArge-scale Video Interpolation Benchmark (LAVIB) for
the low-level video task of video frame interpolation (VFI). LAVIB comprises a
large collection of high-resolution videos sourced from the web through an
automated pipeline with minimal requirements for human verification. Metrics
are computed for each video's motion magnitudes, luminance conditions, frame
sharpness, and contrast. The collection of videos and the creation of
quantitative challenges based on these metrics are under-explored by current
low-level video task datasets. In total, LAVIB includes 283K clips from 17K
ultra-HD videos, covering 77.6 hours. Benchmark train, val, and test sets
maintain similar video metric distributions. Further splits are also created
for out-of-distribution (OOD) challenges, with train and test splits including
videos of dissimilar attributes.

该论文介绍了一种用于视频逐帧插值（VFI）低级视频任务的大规模视频插值基准（LAVIB）。LAVIB 由网上获取的大量高分辨率视频组成，通过自动化流程与人工验证的最小要求。对于每个视频的运动幅度，亮度条件，帧清晰度和对比度进行度量。这些度量指标所基于的视频集合和创建的定量挑战在当前低级视频任务数据集中尚未得到充分探索。总共，LAVIB 包括来自 17K 个超高清视频的 283K 个剪辑，覆盖 77.6 小时。基准培训、验证和测试集的视频度量分布相似。还为 out-of-distribution（OOD）挑战创建了进一步的拆分，其中培训和测试拆分包括具有不同属性的视频。