EditBoard: Towards a Comprehensive Evaluation Benchmark for Text-Based Video Editing Models
Yupeng Chen, Penglin Chen, Xiaoyu Zhang, Yixian Huang, Qian Xie
Abstract
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.
BibTeX
@article{Chen_Chen_Zhang_Huang_Xie_2025, title={EditBoard: Towards a Comprehensive Evaluation Benchmark for Text-Based Video Editing Models}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33754}, DOI={10.1609/aaai.v39i15.33754}, abstractNote={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.}, number={15}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Chen, Yupeng and Chen, Penglin and Zhang, Xiaoyu and Huang, Yixian and Xie, Qian}, year={2025}, month={Apr.}, pages={15975-15983} }