Human-centric video frame interpolation has great potential for improving people's entertainment experiences and finding commercial applications in the sports analysis industry, e.g., synthesizing slow-motion videos. Although there are multiple benchmark datasets available in the community, none of them is dedicated for human-centric scenarios. To bridge this gap, we introduce SportsSloMo, a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution ($\geq$720p) slow-motion sports videos crawled from YouTube. We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets. It highlights the difficulty of our benchmark and suggests that it poses significant challenges even for the best-performing methods, as human bodies are highly deformable and occlusions are frequent in sports videos. To improve the accuracy, we introduce two loss terms considering the human-aware priors, where we add auxiliary supervision to panoptic segmentation and human keypoints detection, respectively. The loss terms are model agnostic and can be easily plugged into any video frame interpolation approaches. Experimental results validate the effectiveness of our proposed loss terms, leading to consistent performance improvement over 5 existing models, which establish strong baseline models on our benchmark. The dataset and code can be found at: https://neu-vi.github.io/SportsSlomo/.

翻译：人眼中心视频帧插值在提升娱乐体验及体育分析产业的商业应用中具有巨大潜力，例如合成慢动作视频。尽管学术界存在多个基准数据集，但尚无专门针对人眼中心场景的数据集。为填补这一空白，我们提出SportsSloMo基准，该数据集包含从YouTube爬取的超过13万个视频片段及100万帧高分辨率（≥720p）慢动作体育视频。我们基于该基准重新训练了多种最先进方法，结果显示其精度较其他数据集有所下降。这一现象凸显了本基准的难度，表明即使是性能最佳的方法也面临重大挑战——体育视频中人体高度形变且遮挡频繁。为提升精度，我们引入两项基于人眼先验的损失项，分别针对全景分割与人体关键点检测添加辅助监督。这些损失项具有模型无关性，可无缝嵌入任意视频帧插值方法。实验验证了所提损失项的有效性，在5个现有模型上实现了一致的性能提升，并为本基准建立了强基线模型。数据集与代码开源地址：https://neu-vi.github.io/SportsSlomo/。