arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2409.09668cs.CV

EditBoard:面向基于文本的视频编辑模型的综合评估基准

EditBoard: Towards a Comprehensive Evaluation Benchmark for Text-Based Video Editing Models

University of Oxford(牛津大学)

Yupeng Chen, Penglin Chen, Xiaoyu Zhang, Yixian Huang, Qian Xie

AI总结:

针对视频编辑模型缺乏综合评估基准的问题,提出EditBoard,涵盖四维度九项自动指标及三项新保真度指标,在四类任务上客观评估模型优缺点,开源以标准化评估。

摘要:

扩散模型的快速发展显著推动了AI生成内容(AIGC)的进步,尤其是在文本到图像(T2I)和文本到视频(T2V)生成方面。基于文本的视频编辑利用这些生成能力,已成为一个前景广阔的领域,能够根据文本提示对视频进行精确修改。尽管创新性视频编辑模型层出不穷,但明显缺乏能够从多个维度全面评估这些模型性能的综合评估基准。现有评估方法有限且不一致,通常用单一分数概括整体性能,这掩盖了模型在单个编辑任务上的有效性。为解决这一缺口,我们提出了EditBoard,这是首个面向基于文本的视频编辑模型的综合评估基准。EditBoard涵盖四个维度上的九项自动指标,在四个任务类别上评估模型,并引入了三项新指标来评估保真度。这一面向任务的基准通过详细描述模型性能并提供对每个模型优缺点的洞察,促进了客观评估。通过开源EditBoard,我们旨在标准化评估并推动鲁棒视频编辑模型的发展。

英文摘要:

The rapid development of diffusion models has significantly advanced AI-generated content (AIGC), particularly in Text-to-Image (T2I) and Text-to-Video (T2V) generation. Text-based video editing, leveraging these generative capabilities, has emerged as a promising field, enabling precise modifications to videos based on text prompts. Despite the proliferation of innovative video editing models, there is a conspicuous lack of comprehensive evaluation benchmarks that holistically assess these models' performance across various dimensions. Existing evaluations are limited and inconsistent, typically summarizing overall performance with a single score, which obscures models' effectiveness on individual editing tasks. To address this gap, we propose EditBoard, the first comprehensive evaluation benchmark for text-based video editing models. EditBoard encompasses nine automatic metrics across four dimensions, evaluating models on four task categories and introducing three new metrics to assess fidelity. This task-oriented benchmark facilitates objective evaluation by detailing model performance and providing insights into each model's strengths and weaknesses. By open-sourcing EditBoard, we aim to standardize evaluation and advance the development of robust video editing models.

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。

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