FiVE-Bench: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models

Minghan Li, Chenxi Xie, Yichen Wu, Lei Zhang, Mengyu Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 16672-16681

Numerous text-to-video (T2V) editing methods have emerged recently, but the lack of a standardized benchmark for fair evaluation has led to inconsistent claims and an inability to assess model sensitivity to hyperparameters. Fine-grained video editing is crucial for enabling precise, object-level modifications while maintaining context and temporal consistency. To address this, we introduce FiVE-Bench, a Fine-grained Video Editing Benchmark for evaluating emerging diffusion and rectified flow models. Our benchmark includes 74 real-world videos and 26 generated videos, featuring 6 fine-grained editing types, 420 object-level editing prompt pairs, and their corresponding masks. Additionally, we adapt the latest rectified flow (RF) T2V generation models--Pyramid-Flow [??] and Wan2.1 [??]--by introducing FlowEdit [??], resulting in training-free and inversion-free video editing models Pyramid-Edit and Wan-Edit. We compare five diffusion methods with our two RF methods on the proposed FiVE-Bench, evaluating them across 15 metrics. These metrics include background preservation, text-video similarity, temporal consistency, and generated video quality. To further enhance object-level evaluation, we introduce FiVE-Acc, a novel metric leveraging Vision-Language Models (VLMs) to assess the success of fine-grained video editing. Experimental results demonstrate that RF-based editing significantly outperforms diffusion-based methods, with Wan-Edit achieving the best overall performance and exhibiting the least sensitivity to hyperparameters. More video demo available on the website: https://sites.google.com/view/five-benchmark.