VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
Xiangbo Gao, Sicong Jiang, Bangya Liu, Xinghao Chen, Minglai Yang, Siyuan Yang, Mingyang Wu, Jiongze Yu, Qi Zheng, Haozhi Wang, Jiayi Zhang, Jie Yang, Zihan Wang, Qing Yin, and Zhengzhong Tu

TL;DR
VEFX-Bench introduces a comprehensive dataset, a specialized reward model, and a benchmark for evaluating AI-assisted video editing systems, addressing the lack of standardized evaluation tools.
Contribution
The paper presents VEFX-Dataset, VEFX-Reward, and VEFX-Bench, enabling standardized, human-aligned assessment of video editing quality and system performance.
Findings
VEFX-Reward correlates better with human judgments than existing models.
Benchmarking reveals current systems struggle with visual plausibility and instruction adherence.
The dataset covers 5,049 examples across 9 editing categories with detailed labels.
Abstract
As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured footage to meet professional requirements. Yet the field still lacks both a large-scale human-annotated dataset with complete editing examples and a standardized evaluator for comparing editing systems. Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels, while current evaluation often relies on expensive manual inspection or generic vision-language model judges that are not specialized for editing quality. We introduce VEFX-Dataset, a human-annotated dataset containing 5,049 video editing examples across 9 major editing categories and 32 subcategories, each labeled along three decoupled dimensions: Instruction Following, Rendering Quality, and Edit Exclusivity. Building on…
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