HoloCine: Holistic Generation of Cinematic Multi-Shot Long Video Narratives
Yihao Meng, Hao Ouyang, Yue Yu, Qiuyu Wang, Wen Wang, Ka Leong Cheng, Hanlin Wang, Yixuan Li, Cheng Chen, Yanhong Zeng, Yujun Shen, Huamin Qu

TL;DR
HoloCine introduces a holistic model for generating long, coherent cinematic videos with global consistency, directorial control, and emergent cinematic understanding, advancing automated filmmaking.
Contribution
It presents a novel architecture with Window Cross-Attention and Sparse Inter-Shot Self-Attention for coherent, long-form video narrative generation, surpassing previous clip-based models.
Findings
Achieves state-of-the-art narrative coherence in long videos
Develops persistent memory for characters and scenes
Demonstrates emergent cinematic techniques understanding
Abstract
State-of-the-art text-to-video models excel at generating isolated clips but fall short of creating the coherent, multi-shot narratives, which are the essence of storytelling. We bridge this "narrative gap" with HoloCine, a model that generates entire scenes holistically to ensure global consistency from the first shot to the last. Our architecture achieves precise directorial control through a Window Cross-Attention mechanism that localizes text prompts to specific shots, while a Sparse Inter-Shot Self-Attention pattern (dense within shots but sparse between them) ensures the efficiency required for minute-scale generation. Beyond setting a new state-of-the-art in narrative coherence, HoloCine develops remarkable emergent abilities: a persistent memory for characters and scenes, and an intuitive grasp of cinematic techniques. Our work marks a pivotal shift from clip synthesis towards…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Video Analysis and Summarization · Multimodal Machine Learning Applications
