Can video generation replace cinematographers? Research on the cinematic language of generated video
Xiaozhe Li, Kai WU, Siyi Yang, YiZhan Qu, Guohua.Zhang, Zhiyu Chen,, Jiayao Li, Jiangchuan Mu, Xiaobin Hu, Wen Fang, Mingliang Xiong, Hao Deng,, Qingwen Liu, Gang Li, Bin He

TL;DR
This paper advances text-to-video generation by integrating cinematic language control through a new dataset, specialized models, and evaluation methods, aiming to produce videos with professional cinematic quality.
Contribution
It introduces a cinematic language dataset, CameraDiff for control, and CameraCLIP with CLIPLoRA for evaluation and style blending, enhancing cinematic coherence in generated videos.
Findings
CameraDiff provides stable cinematic control.
CameraCLIP achieves high cinematic alignment accuracy.
CLIPLoRA improves multi-shot style blending.
Abstract
Recent advancements in text-to-video (T2V) generation have leveraged diffusion models to enhance visual coherence in videos synthesized from textual descriptions. However, existing research primarily focuses on object motion, often overlooking cinematic language, which is crucial for conveying emotion and narrative pacing in cinematography. To address this, we propose a threefold approach to improve cinematic control in T2V models. First, we introduce a meticulously annotated cinematic language dataset with twenty subcategories, covering shot framing, shot angles, and camera movements, enabling models to learn diverse cinematic styles. Second, we present CameraDiff, which employs LoRA for precise and stable cinematic control, ensuring flexible shot generation. Third, we propose CameraCLIP, designed to evaluate cinematic alignment and guide multi-shot composition. Building on CameraCLIP,…
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Taxonomy
TopicsCinema and Media Studies
MethodsDiffusion
