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This paper introduces CineWeaver, a training-free framework for generating long, reference-controllable multi-shot videos that addresses the limitations of existing text-to-video diffusion models. By manipulating positional encoding and attention patterns, CineWeaver breaks the structural bias toward temporal continuity, allowing for distinct shot transitions while maintaining character and scene control. Experimental results confirm that CineWeaver achieves high-quality cinematic video generation with consistent identities and stable global appearances across extended durations.
CineWeaver breaks the mold by enabling long-form, reference-controllable, and multi-shot video generation without the need for retraining, setting a new standard in cinematic storytelling.
Cinematic video generation is challenging for text-to-video diffusion models due to concurrent requirements on multi-shot generation, fine-grained controllability over characters and scenes, and long-form generation across extended temporal horizons. Existing methods rely on customization and retraining to separately address specific requirements, and cannot simultaneously fulfill all the requirements with a unified framework. In this paper, we shed light on the training-free paradigm with the key insight that the difficulty of multi-shot generation arises from a structural bias toward temporal continuity in pretrained video diffusion models, and consequently, propose a unified framework named CineWeaver to achieve reference-controllable multi-shot long-video generation without retraining. We manipulate positional encoding and attention patterns to break temporal continuity during inference to enable clear shot transitions using pretrained video diffusion models. Furthermore, we extend the proposed framework with a shot-routed reference conditioning mechanism for per-shot fine-grained controllability, and develop an anchor memory mechanism to allow long-form generation with consistent global appearance cues. To our best knowledge, CineWeaver is the first unified framework to simultaneously enable \textbf{long-form}, \textbf{reference-controllable}, and \textbf{multi-shot} video generation in a training-free fashion. Experimental results demonstrate that CineWeaver produces high-quality cinematic videos of long durations with consistent identities, stable global appearance, and clear shot transitions. The project page is available at: https://cineweaver.github.io.
