2024/09/15 by Yupeng Chen, Penglin Chen, Chen, Yupeng +7 · 3 citations
Arts and Humanities · Computer Science · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Digital Humanities and Scholarship #Digital Rights Management and Security #FOS: Computer and information sciences #Multimedia Communication and Technology
paper · doi:10.48550/arxiv.2409.09668
openalex publication_date 2024/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.