ShotBench: Expert-Level Cinematic Understanding in Vision-Language
  Models
Authors: name: Hongbo Liu, name: Jingwen He, name: Yi Jin, name: Dian Zheng, name: Yuhao Dong, name: Fan Zhang, name: Ziqi Huang, name: Yinan He, name: Yangguang Li, name: Weichao Chen, name: Yu Qiao, name: Wanli Ouyang, name: Shengjie Zhao, name: Ziwei Liu

Cinematography, the fundamental visual language of film, is essential for
conveying narrative, emotion, and aesthetic quality. While recent
Vision-Language Models (VLMs) demonstrate strong general visual understanding,
their proficiency in comprehending the nuanced cinematic grammar embedded
within individual shots remains largely unexplored and lacks robust evaluation.
This critical gap limits both fine-grained visual comprehension and the
precision of AI-assisted video generation. To address this, we introduce
ShotBench, a comprehensive benchmark specifically designed for cinematic
language understanding. It features over 3.5k expert-annotated QA pairs from
images and video clips, meticulously curated from over 200 acclaimed
(predominantly Oscar-nominated) films and spanning eight key cinematography
dimensions. Our evaluation of 24 leading VLMs on ShotBench reveals their
substantial limitations: even the top-performing model achieves less than 60%
average accuracy, particularly struggling with fine-grained visual cues and
complex spatial reasoning. To catalyze advancement in this domain, we construct
ShotQA, a large-scale multimodal dataset comprising approximately 70k cinematic
QA pairs. Leveraging ShotQA, we develop ShotVL through supervised fine-tuning
and Group Relative Policy Optimization. ShotVL significantly outperforms all
existing open-source and proprietary models on ShotBench, establishing new
state-of-the-art performance. We open-source our models, data, and code to
foster rapid progress in this crucial area of AI-driven cinematic understanding
and generation.