Text-driven video editing has recently experienced rapid development. Despite this, evaluating edited videos remains a considerable challenge. Current metrics tend to fail to align with human perceptions, and effective quantitative metrics for video editing are still notably absent. To address this, we introduce E-Bench, a benchmark suite tailored to the assessment of text-driven video editing. This suite includes E-Bench DB, a video quality assessment (VQA) database for video editing. E-Bench DB encompasses a diverse set of source videos featuring various motions and subjects, along with multiple distinct editing prompts, editing results from 8 different models, and the corresponding Mean Opinion Scores (MOS) from 24 human annotators. Based on E-Bench DB, we further propose E-Bench QA, a quantitative human-aligned measurement for the text-driven video editing task. In addition to the aesthetic, distortion, and other visual quality indicators that traditional VQA methods emphasize, E-Bench QA focuses on the text-video alignment and the relevance modeling between source and edited videos. It proposes a new assessment network for video editing that attains superior performance in alignment with human preferences. To the best of our knowledge, E-Bench introduces the first quality assessment dataset for video editing and an effective subjective-aligned quantitative metric for this domain. All data and code will be publicly available at https://github.com/littlespray/E-Bench.

翻译：文本驱动视频编辑技术近期发展迅速。然而，对编辑后视频的质量评估仍面临重大挑战。现有评估指标往往与人类主观感知存在偏差，且该领域仍缺乏有效的量化评估标准。为此，我们提出了E-Bench——一个专门针对文本驱动视频编辑评估的基准套件。该套件包含E-Bench DB，这是一个面向视频编辑任务的视频质量评估数据库。E-Bench DB涵盖了包含多样化运动与主体的源视频、多个差异化编辑提示、来自8个不同模型的编辑结果，以及24位人工标注者给出的平均意见分数。基于E-Bench DB，我们进一步提出了E-Bench QA——一种面向文本驱动视频编辑任务且与人类感知对齐的量化评估方法。除了传统视频质量评估方法关注的美学质量、失真度等视觉指标外，E-Bench QA重点建模文本-视频对齐度及源视频与编辑视频间的关联性，并提出了一种新的视频编辑评估网络，该网络在贴合人类偏好方面表现出优越性能。据我们所知，E-Bench首次为视频编辑领域引入了质量评估数据集及有效的主观对齐量化指标。所有数据与代码将在https://github.com/littlespray/E-Bench 公开。