This paper focuses on understanding the predominant video creation pipeline,
i.e., compositional video editing with six main types of editing components,
including video effects, animation, transition, filter, sticker, and text. In
contrast to existing visual representation learning of visual materials (i.e.,
images/videos), we aim to learn visual representations of editing
actions/components that are generally applied on raw materials. We start by
proposing the first large-scale dataset for editing components of video
creation, which covers about $3,094$ editing components with $618,800$ videos.
Each video in our dataset is rendered by various image/video materials with a
single editing component, which supports atomic visual understanding of
different editing components. It can also benefit several downstream tasks,
e.g., editing component recommendation, editing component
recognition/retrieval, etc. Existing visual representation methods perform
poorly because it is difficult to disentangle the visual appearance of editing
components from raw materials. To that end, we benchmark popular alternative
solutions and propose a novel method that learns to attend to the appearance of
editing components regardless of raw materials. Our method achieves favorable
results on editing component retrieval/recognition compared to the alternative
solutions. A user study is also conducted to show that our representations
cluster visually similar editing components better than other alternatives.
Furthermore, our learned representations used to transition recommendation
tasks achieve state-of-the-art results on the AutoTransition dataset. The code
and dataset will be released for academic use.

该研究聚焦于理解主要的视频制作流程，包括六种主要的编辑组件：视频效果、动画、过渡、滤镜、贴纸和文本。该论文介绍了第一个用于视频创作的大规模数据集，探索了编辑组件的视觉表示方法，并提出了一种新的学习方法，能够更好地学习与编辑组件相关的视觉特征。研究结果表明，该方法在编辑组件的检索和识别方面表现优于其他替代解决方案，并且在过渡推荐任务上取得了最先进的结果。

Edit3K: 视频编辑组件的通用表示学习

Edit3K: Universal Representation Learning for Video Editing Components

We propose an automatic approach that extracts editing styles in a source
video and applies the edits to matched footage for video creation. Our Computer
Vision based techniques considers framing, content type, playback speed, and
lighting of each input video segment. By applying a combination of these
features, we demonstrate an effective method that automatically transfers the
visual and temporal styles from professionally edited videos to unseen raw
footage. We evaluated our approach with real-world videos that contained a
total of 3872 video shots of a variety of editing styles, including different
subjects, camera motions, and lighting. We reported feedback from survey
participants who reviewed a set of our results.

本文提出了一种计算机视觉技术，能够自动提取源视频中的编辑样式，并将其应用到匹配的素材上，以实现视频创作。通过采用不同的特征组合，本研究证明了一种有效的方法，可以自动将视觉和时间风格从专业的编辑视频转移到未经处理的视频素材上。该方法在多个实际场景的视频素材中进行了测试并获得了正面的反馈。