Related papers: CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation

CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation

URL: http://arxiv.org/abs/2505.15145v1
Date: Wed, 21 May 2025 06:02:39 GMT
Title: CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation
Authors: Xinran Wang, Songyu Xu, Xiangxuan Shan, Yuxuan Zhang, Muxi Diao, Xueyan Duan, Yanhua Huang, Kongming Liang, Zhanyu Ma,
Abstract summary: CineTechBench is a benchmark founded on precise, manual annotation by seasoned cinematography experts.<n>Our benchmark covers seven essential aspects-shot scale, shot angle, composition, camera movement, lighting, color, and focal length.<n>For the generation task, we assess advanced video generation models on their capacity to reconstruct cinema-quality camera movements.
Score: 22.88243961225531
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Cinematography is a cornerstone of film production and appreciation, shaping mood, emotion, and narrative through visual elements such as camera movement, shot composition, and lighting. Despite recent progress in multimodal large language models (MLLMs) and video generation models, the capacity of current models to grasp and reproduce cinematographic techniques remains largely uncharted, hindered by the scarcity of expert-annotated data. To bridge this gap, we present CineTechBench, a pioneering benchmark founded on precise, manual annotation by seasoned cinematography experts across key cinematography dimensions. Our benchmark covers seven essential aspects-shot scale, shot angle, composition, camera movement, lighting, color, and focal length-and includes over 600 annotated movie images and 120 movie clips with clear cinematographic techniques. For the understanding task, we design question answer pairs and annotated descriptions to assess MLLMs' ability to interpret and explain cinematographic techniques. For the generation task, we assess advanced video generation models on their capacity to reconstruct cinema-quality camera movements given conditions such as textual prompts or keyframes. We conduct a large-scale evaluation on 15+ MLLMs and 5+ video generation models. Our results offer insights into the limitations of current models and future directions for cinematography understanding and generation in automatically film production and appreciation. The code and benchmark can be accessed at https://github.com/PRIS-CV/CineTechBench.

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