Artificial Intelligence
arXiv
Yinan Chen, Jiangning Zhang, Teng Hu, Yuxiang Zeng, Zhucun Xue, Qingdong He, Chengjie Wang, Yong Liu, Xiaobin Hu, Shuicheng Yan
13 Oct 2025 • 3 min read
AI-generated image, based on the article abstract
Quick Insight
IVEBench: The New Playground for Smarter Video Editing
Ever wondered how your phone could turn a simple clip into a movie just by following a short instruction? Scientists have built a fresh testing ground called IVEBench that lets AI master instruction‑guided video editing exactly the w…
Artificial Intelligence
arXiv
Yinan Chen, Jiangning Zhang, Teng Hu, Yuxiang Zeng, Zhucun Xue, Qingdong He, Chengjie Wang, Yong Liu, Xiaobin Hu, Shuicheng Yan
13 Oct 2025 • 3 min read
AI-generated image, based on the article abstract
Quick Insight
IVEBench: The New Playground for Smarter Video Editing
Ever wondered how your phone could turn a simple clip into a movie just by following a short instruction? Scientists have built a fresh testing ground called IVEBench that lets AI master instruction‑guided video editing exactly the way you ask. Imagine a kitchen where chefs practice recipes on 600 different ingredients—IVEBench offers 600 varied video “ingredients,” from a few seconds to longer scenes, covering everything from sports to sunsets. The suite challenges AI with 8 big editing categories and 35 tiny tasks, like “add a dramatic zoom” or “swap the background,” all written and polished by smart language models and human experts. To judge the results, IVEBench looks at three things: how good the video looks, whether it followed the instruction, and if it stayed true to the original footage. This three‑point check is like a movie critic, a teacher, and a detective rolled into one. With this tool, developers can create video editors that feel more natural and reliable, bringing professional‑grade magic to everyday creators. The future of storytelling is just a command away.
Article Short Review
Overview
The article presents IVEBench, a novel benchmark suite designed for instruction-guided video editing (IVE). It addresses significant shortcomings in existing benchmarks, such as limited source diversity and inadequate evaluation metrics. IVEBench features a comprehensive dataset of 600 high-quality videos across seven semantic dimensions and includes eight categories of editing tasks. The evaluation framework integrates traditional metrics with multimodal assessments, focusing on video quality, instruction compliance, and fidelity. The findings indicate that IVEBench effectively aligns with human perception, enhancing the evaluation of IVE methods.
Critical Evaluation
Strengths
One of the primary strengths of IVEBench is its extensive and diverse dataset, which significantly enhances the robustness of evaluations in video editing research. The inclusion of various editing tasks and the three-dimensional evaluation protocol provide a comprehensive framework that addresses the limitations of previous benchmarks. Furthermore, the integration of large language models for prompt generation ensures that the tasks are relevant and challenging, reflecting real-world editing scenarios.
Weaknesses
Despite its strengths, IVEBench is not without limitations. The article notes that while the benchmark demonstrates good frame-to-frame consistency, there are issues with per-frame quality and instruction compliance. These weaknesses suggest that while IVEBench is a step forward, there is still room for improvement in the fidelity of the editing outputs. Additionally, the reliance on specific models for evaluation may introduce biases that could affect the generalizability of the findings.
Implications
The introduction of IVEBench has significant implications for the field of video editing research. By providing a more comprehensive and human-aligned evaluation framework, it sets a new standard for assessing IVE methods. This benchmark not only facilitates better comparisons among state-of-the-art models but also encourages future research to focus on enhancing fidelity and expanding the range of editing capabilities.
Conclusion
In summary, IVEBench represents a critical advancement in the evaluation of instruction-guided video editing. Its comprehensive dataset and innovative evaluation metrics offer a valuable resource for researchers and practitioners alike. As the field continues to evolve, the insights gained from IVEBench will be instrumental in guiding future developments and improving the overall quality of video editing technologies.
Readability
The article is well-structured and presents its findings in a clear and engaging manner. The use of concise paragraphs and straightforward language enhances readability, making it accessible to a broad audience. By focusing on key terms and concepts, the text effectively communicates the significance of IVEBench in advancing the field of video editing.
Article Comprehensive Review
Overview
The article presents IVEBench, a novel benchmark suite designed for instruction-guided video editing (IVE), addressing significant shortcomings in existing evaluation frameworks. It highlights the limitations of current benchmarks, which often lack diversity in source material and comprehensive task coverage. IVEBench features a robust dataset of 600 high-quality videos, categorized into eight editing tasks with 35 subcategories, all generated through advanced language models and expert review. The benchmark introduces a three-dimensional evaluation protocol that assesses video quality, instruction compliance, and video fidelity, integrating traditional metrics with innovative multimodal assessments. Extensive experiments validate IVEBench’s effectiveness in providing a comprehensive evaluation of state-of-the-art IVE methods, demonstrating its alignment with human perception.
Critical Evaluation
Strengths
One of the primary strengths of IVEBench is its comprehensive approach to addressing the limitations of existing benchmarks in the field of instruction-guided video editing. By incorporating a diverse dataset of 600 videos, the benchmark ensures a wide range of scenarios and editing tasks, which enhances the robustness of the evaluation process. The inclusion of eight categories of editing tasks, along with 35 subcategories, allows for a nuanced assessment of various editing capabilities, making it a valuable tool for researchers and practitioners alike.
Moreover, the three-dimensional evaluation protocol established by IVEBench is a significant advancement in the field. By evaluating video quality, instruction compliance, and video fidelity, the benchmark provides a holistic view of the performance of video editing models. This multidimensional approach not only aligns with human perception but also integrates traditional metrics with modern assessments based on large language models, thereby enhancing the relevance and applicability of the findings.
Weaknesses
Despite its strengths, IVEBench is not without limitations. One notable weakness is the potential for bias in the dataset selection and prompt generation processes. While the article mentions that prompts are refined through expert review, the reliance on a limited pool of experts may inadvertently introduce subjective biases that could affect the evaluation outcomes. Additionally, the dataset’s diversity, while extensive, may still not encompass all possible editing scenarios, which could limit the generalizability of the benchmark’s findings.
Another concern is the potential for overfitting in the evaluation metrics. The article indicates that while IVEBench demonstrates good frame-to-frame consistency, there are issues with per-frame quality and instruction compliance. This suggests that while the benchmark may excel in certain areas, it may not fully capture the complexities of human-aligned video editing, necessitating further refinement of the evaluation metrics to ensure they accurately reflect real-world editing scenarios.
Caveats
Biases in the evaluation process can stem from various sources, including the selection of videos and the criteria used for assessing editing quality. The article acknowledges the challenges in achieving a truly representative dataset, which raises questions about the potential for bias in the evaluation outcomes. Furthermore, the reliance on human evaluators for assessing instruction compliance and video fidelity may introduce variability based on individual perceptions and preferences, which could skew the results.
Implications
The introduction of IVEBench has significant implications for the field of instruction-guided video editing. By providing a more comprehensive and human-aligned evaluation framework, it sets a new standard for assessing video editing models. This benchmark not only facilitates the comparison of different editing methods but also encourages the development of more sophisticated models that can better meet user expectations and editing requirements.
Furthermore, the findings from the extensive experiments conducted using IVEBench highlight the need for ongoing improvements in video editing technologies. The identification of common artifacts and limitations in current models underscores the importance of continuous innovation and refinement in the field. As researchers and practitioners adopt IVEBench, it is likely to drive advancements in IVE methods, ultimately leading to more intuitive and effective video editing solutions.
Conclusion
In summary, the article presents IVEBench as a groundbreaking benchmark suite for instruction-guided video editing, addressing critical gaps in existing evaluation frameworks. Its comprehensive dataset, multidimensional evaluation protocol, and alignment with human perception position it as a valuable resource for researchers and practitioners in the field. While there are notable strengths, such as its diverse dataset and innovative assessment methods, the potential for bias and limitations in generalizability warrant careful consideration. Overall, IVEBench represents a significant step forward in the evaluation of video editing technologies, with the potential to shape future research and development in this rapidly evolving domain.