VE-Bench：面向文本驱动视频编辑质量评估的主观对齐基准套件
VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
AI总结：
本文提出VE-Bench基准套件，包含视频编辑质量评估数据库VE-Bench DB和定量评估方法VE-Bench QA，通过关注文本-视频对齐和源视频与编辑视频相关性，实现了与人类偏好对齐的视频编辑质量评估。
摘要：
文本驱动的视频编辑近期经历了快速发展。尽管如此，评估编辑后的视频仍然是一个巨大的挑战。当前的指标往往无法与人类感知对齐，并且仍然明显缺乏用于视频编辑的有效定量指标。为了解决这个问题，我们引入了VE-Bench，一个为文本驱动视频编辑评估量身定制的基准套件。该套件包括VE-Bench DB，一个用于视频编辑的视频质量评估（VQA）数据库。VE-Bench DB包含一组多样的源视频，具有各种运动和主体，以及多个不同的编辑提示，来自8个不同模型的编辑结果，以及来自24个人类标注者的相应平均意见分数（MOS）。基于VE-Bench DB，我们进一步提出了VE-Bench QA，一个用于文本驱动视频编辑任务的定量人类对齐测量方法。除了传统VQA方法强调的美学、失真和其他视觉质量指标外，VE-Bench QA还关注文本-视频对齐以及源视频和编辑视频之间的相关性建模。它提出了一个新的视频编辑评估网络，在与人类偏好对齐方面取得了卓越的性能。据我们所知，VE-Bench引入了第一个用于视频编辑的质量评估数据集和该领域的有效主观对齐定量指标。所有数据和代码将在https://github.com/littlespray/VE-Bench上公开提供。
英文摘要：
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 VE-Bench, a benchmark suite tailored to the assessment of text-driven video editing. This suite includes VE-Bench DB, a video quality assessment (VQA) database for video editing. VE-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 VE-Bench DB, we further propose VE-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, VE-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, VE-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/VE-Bench.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
