@inproceedings{shi-etal-2026-msvbench,
title = "{MSVB}ench: Towards Human-Level Evaluation of Multi-Shot Video Generation",
author = "Shi, Haoyuan and
li, Yunxin and
Deng, Nanhao and
Xu, Zhenran and
Chen, Xinyu and
Wang, Longyue and
Hu, Baotian and
Zhang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1203/",
pages = "24034--24058",
ISBN = "979-8-89176-395-1",
abstract = "The evolution of video generation toward complex, multi-shot narratives has exposed a critical deficit in current evaluation methods. Existing benchmarks remain anchored to single-shot paradigms, lacking the comprehensive story assets and cross-shot metrics required to assess long-form coherence and appeal. To bridge this gap, we introduce $\textbf{MSVBench}$, the first comprehensive benchmark featuring hierarchical scripts and reference images tailored for $\textbf{M}$ulti-$\textbf{S}$hot $\textbf{V}$ideo generation. We propose a hybrid evaluation framework that synergizes the high-level semantic reasoning of Large Multimodal Models (LMMs) with the fine-grained perceptual rigor of domain-specific expert models. Evaluating 20 video generation methods across diverse paradigms, we find that current models{---}despite strong visual fidelity{---}primarily behave as visual interpolators rather than true world models. We further validate the reliability of our benchmark by demonstrating a state-of-the-art Spearman{'}s rank correlation of $\textbf{0.944}$ with human judgments. Finally, MSVBench extends beyond evaluation by providing a scalable supervisory signal. Fine-tuning a lightweight model on its pipeline-refined reasoning traces yields human-aligned performance comparable to commercial models like Gemini-2.5-Flash."
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<abstract>The evolution of video generation toward complex, multi-shot narratives has exposed a critical deficit in current evaluation methods. Existing benchmarks remain anchored to single-shot paradigms, lacking the comprehensive story assets and cross-shot metrics required to assess long-form coherence and appeal. To bridge this gap, we introduce MSVBench, the first comprehensive benchmark featuring hierarchical scripts and reference images tailored for Multi-Shot Video generation. We propose a hybrid evaluation framework that synergizes the high-level semantic reasoning of Large Multimodal Models (LMMs) with the fine-grained perceptual rigor of domain-specific expert models. Evaluating 20 video generation methods across diverse paradigms, we find that current models—despite strong visual fidelity—primarily behave as visual interpolators rather than true world models. We further validate the reliability of our benchmark by demonstrating a state-of-the-art Spearman’s rank correlation of 0.944 with human judgments. Finally, MSVBench extends beyond evaluation by providing a scalable supervisory signal. Fine-tuning a lightweight model on its pipeline-refined reasoning traces yields human-aligned performance comparable to commercial models like Gemini-2.5-Flash.</abstract>
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%0 Conference Proceedings
%T MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation
%A Shi, Haoyuan
%A li, Yunxin
%A Deng, Nanhao
%A Xu, Zhenran
%A Chen, Xinyu
%A Wang, Longyue
%A Hu, Baotian
%A Zhang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shi-etal-2026-msvbench
%X The evolution of video generation toward complex, multi-shot narratives has exposed a critical deficit in current evaluation methods. Existing benchmarks remain anchored to single-shot paradigms, lacking the comprehensive story assets and cross-shot metrics required to assess long-form coherence and appeal. To bridge this gap, we introduce MSVBench, the first comprehensive benchmark featuring hierarchical scripts and reference images tailored for Multi-Shot Video generation. We propose a hybrid evaluation framework that synergizes the high-level semantic reasoning of Large Multimodal Models (LMMs) with the fine-grained perceptual rigor of domain-specific expert models. Evaluating 20 video generation methods across diverse paradigms, we find that current models—despite strong visual fidelity—primarily behave as visual interpolators rather than true world models. We further validate the reliability of our benchmark by demonstrating a state-of-the-art Spearman’s rank correlation of 0.944 with human judgments. Finally, MSVBench extends beyond evaluation by providing a scalable supervisory signal. Fine-tuning a lightweight model on its pipeline-refined reasoning traces yields human-aligned performance comparable to commercial models like Gemini-2.5-Flash.
%U https://aclanthology.org/2026.findings-acl.1203/
%P 24034-24058
Markdown (Informal)
[MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation](https://aclanthology.org/2026.findings-acl.1203/) (Shi et al., Findings 2026)
ACL
- Haoyuan Shi, Yunxin li, Nanhao Deng, Zhenran Xu, Xinyu Chen, Longyue Wang, Baotian Hu, and Min Zhang. 2026. MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 24034–24058, San Diego, California, United States. Association for Computational Linguistics.