Chen, Y., Chen, P., Zhang, X. et al. (2 more authors) (2025) EditBoard: Towards a Comprehensive Evaluation Benchmark for Text-Based Video Editing Models. In: Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence. Thirty-Ninth AAAI Conference on Artificial Intelligence, 25 Feb - 04 Mar 2025, Philadelphia, Pennsylvania. Association for the Advancement of Artificial Intelligence, Washington, DC, pp. 15975-15983. ISBN: 978-1-57735-897-8. ISSN: 2159-5399. EISSN: 2374-3468.
Abstract
The rapid development of diffusion models has significantly advanced AI-generated content (AIGC), particularly in Textto-Image (T2I) and Text-to-Video (T2V) generation. Textbased video editing, leveraging these generative capabilities, has emerged as a promising field, enabling precise modifications to video content based on textual prompts. Despite the proliferation of innovative video editing models, there is a conspicuous lack of comprehensive evaluation frameworks that holistically assess these models’ performance across various dimensions. Existing metrics are limited, inconsistent, and focused on assigning a single score per metric, failing to reveal model’s performance on each editing task. 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 key dimensions, evaluating models on four categories of tasks, and introduces three new metrics to assess fidelity. This task-oriented framework facilitates objective evaluation by breaking down model performance into details, 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a proceedings paper published in Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence. Uploaded with permission from the publisher. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Date Deposited: | 19 Sep 2025 15:29 |
Last Modified: | 01 Oct 2025 10:24 |
Published Version: | https://ojs.aaai.org/index.php/AAAI/article/view/3... |
Status: | Published |
Publisher: | Association for the Advancement of Artificial Intelligence |
Identification Number: | 10.1609/aaai.v39i15.33754 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:231586 |