Can video generation replace cinematographers? Research on the cinematic language of generated video

2024-12-16

arXiv.org

AI Summary

Existing text-to-video generation models neglect cinematic language—such as framing, camera angle, and camera motion—limiting their capacity for professional narrative expression. To address this, we systematically define and annotate 20 cinematic elements, establishing the first fine-grained cinematic semantics dataset. We propose CameraDiff, a LoRA-based framework for stable, controllable camera parameter generation. We design CameraCLIP to enable precise cinematic semantic retrieval (R@1 = 0.83). Furthermore, we introduce CLIPLoRA—a novel method that leverages CLIP guidance to dynamically compose multiple LoRA modules—enabling intra-video multi-shot semantic alignment and seamless stylistic transitions. Our approach significantly enhances cinematic expressiveness and narrative coherence in generated videos, advancing text-to-video synthesis toward professional filmmaking standards.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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, we introduce CLIPLoRA, a CLIP-guided dynamic LoRA composition method that adaptively fuses multiple pre-trained cinematic LoRAs, enabling smooth transitions and seamless style blending. Experimental results demonstrate that CameraDiff ensures stable and precise cinematic control, CameraCLIP achieves an R@1 score of 0.83, and CLIPLoRA significantly enhances multi-shot composition within a single video, bridging the gap between automated video generation and professional cinematography. extsuperscript{1}

Problem

• Enhancing cinematic language control in text-to-video generation models
• Addressing the neglect of cinematic styles like framing and camera movements
• Bridging the gap between automated video generation and professional cinematography

Innovation

• Annotated dataset for diverse cinematic styles
• CameraDiff with LoRA for precise shot control
• CLIPLoRA for adaptive multi-shot composition