IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing
  Assessment
Instruction-guided video editing has emerged as a rapidly advancing research
direction, offering new opportunities for intuitive content transformation
while also posing significant challenges for systematic evaluation. Existing
video editing benchmarks fail to support the evaluation of instruction-guided
video editing adequately and further suffer from limited source diversity,
narrow task coverage and incomplete evaluation metrics. To address the above
limitations, we introduce IVEBench, a modern benchmark suite specifically
designed for instruction-guided video editing assessment. IVEBench comprises a
diverse database of 600 high-quality source videos, spanning seven semantic
dimensions, and covering video lengths ranging from 32 to 1,024 frames. It
further includes 8 categories of editing tasks with 35 subcategories, whose
prompts are generated and refined through large language models and expert
review. Crucially, IVEBench establishes a three-dimensional evaluation protocol
encompassing video quality, instruction compliance and video fidelity,
integrating both traditional metrics and multimodal large language model-based
assessments. Extensive experiments demonstrate the effectiveness of IVEBench in
benchmarking state-of-the-art instruction-guided video editing methods, showing
its ability to provide comprehensive and human-aligned evaluation outcomes.
68edc14ade1fee572713a891
Yinan Chen
68edc14ade1fee572713a892
Jiangning Zhang
68edc14ade1fee572713a893
Teng Hu
claimed_verified
2025-11-27T10:17:35.161Z
6484576450e57f3530ee6432
/avatars/16a780f94597e9f5a4d57a84879102d7.svg
huteng
JTUplayer
user
JTUplayer
68edc14ade1fee572713a894
Yuxiang Zeng
68edc14ade1fee572713a895
Zhucun Xue
68edc14ade1fee572713a896
Qingdong He
68edc14ade1fee572713a897
Chengjie Wang
68edc14ade1fee572713a898
Yong Liu
68edc14ade1fee572713a899
Xiaobin Hu
68edc14ade1fee572713a89a
Shuicheng Yan
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing
  Assessment
Instruction-guided video editing has emerged as a rapidly advancing research
direction, offering new opportunities for intuitive content transformation
while also posing significant challenges for systematic evaluation. Existing
video editing benchmarks fail to support the evaluation of instruction-guided
video editing adequately and further suffer from limited source diversity,
narrow task coverage and incomplete evaluation metrics. To address the above
limitations, we introduce IVEBench, a modern benchmark suite specifically
designed for instruction-guided video editing assessment. IVEBench comprises a
diverse database of 600 high-quality source videos, spanning seven semantic
dimensions, and covering video lengths ranging from 32 to 1,024 frames. It
further includes 8 categories of editing tasks with 35 subcategories, whose
prompts are generated and refined through large language models and expert
review. Crucially, IVEBench establishes a three-dimensional evaluation protocol
encompassing video quality, instruction compliance and video fidelity,
integrating both traditional metrics and multimodal large language model-based
assessments. Extensive experiments demonstrate the effectiveness of IVEBench in
benchmarking state-of-the-art instruction-guided video editing methods, showing
its ability to provide comprehensive and human-aligned evaluation outcomes.
IVEBench is a benchmark suite for instruction-guided video editing that addresses limitations in existing benchmarks through diverse video sources, comprehensive task coverage, and a multi-dimensional evaluation protocol.
instruction-guided video editing benchmark suite source diversity task coverage evaluation metrics large language models video quality instruction compliance video fidelity multimodal assessments