OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existing multi-shot video generation (MSV) methods struggle to effectively model long-range cross-shot context, as they rely on limited temporal windows or single keyframe conditioning, leading to degraded performance under complex narratives. In this work, we propose OneStory, enabling global yet compact cross-shot context modeling for consistent and scalable narrative generation. OneStory reformulates MSV as a next-shot generation task, enabling autoregressive shot synthesis while leveraging pretrained image-to-video (I2V) models for strong visual conditioning. We introduce two key modules: a Frame Selection module that constructs a semantically-relevant global memory based on informative frames from prior shots, and an Adaptive Conditioner that performs importance-guided patchification to generate compact context for direct conditioning. We further curate a high-quality multi-shot dataset with referential captions to mirror real-world storytelling patterns, and design effective training strategies under the next-shot paradigm. Finetuned from a pretrained I2V model on our curated 60K dataset, OneStory achieves state-of-the-art narrative coherence across diverse and complex scenes in both text- and image-conditioned settings, enabling controllable and immersive long-form video storytelling.
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Zhaochong An
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2025-12-10T13:07:50.839Z
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Zhaochong An
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Menglin Jia
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Haonan Qiu
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Zijian Zhou
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2025-12-15T08:14:03.051Z
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Zijian Zhou
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Xiaoke Huang
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2026-02-26T10:04:24.549Z
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Xiaoke Huang
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xk-huang
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Zhiheng Liu
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Weiming Ren
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2026-01-20T09:41:29.707Z
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Weiming Ren
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Kumara Kahatapitiya
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2025-12-10T10:51:00.011Z
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Kumara Kahatapitiya
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Ding Liu
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Sen He
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Chenyang Zhang
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Tao Xiang
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Fanny Yang
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Serge Belongie
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Tian Xie
OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existing multi-shot video generation (MSV) methods struggle to effectively model long-range cross-shot context, as they rely on limited temporal windows or single keyframe conditioning, leading to degraded performance under complex narratives. In this work, we propose OneStory, enabling global yet compact cross-shot context modeling for consistent and scalable narrative generation. OneStory reformulates MSV as a next-shot generation task, enabling autoregressive shot synthesis while leveraging pretrained image-to-video (I2V) models for strong visual conditioning. We introduce two key modules: a Frame Selection module that constructs a semantically-relevant global memory based on informative frames from prior shots, and an Adaptive Conditioner that performs importance-guided patchification to generate compact context for direct conditioning. We further curate a high-quality multi-shot dataset with referential captions to mirror real-world storytelling patterns, and design effective training strategies under the next-shot paradigm. Finetuned from a pretrained I2V model on our curated 60K dataset, OneStory achieves state-of-the-art narrative coherence across diverse and complex scenes in both text- and image-conditioned settings, enabling controllable and immersive long-form video storytelling.
OneStory generates coherent multi-shot videos by modeling global cross-shot context through a Frame Selection module and an Adaptive Conditioner, leveraging pretrained image-to-video models and a curated dataset.
multi-shot video generation next-shot generation autoregressive shot synthesis pretrained image-to-video models Frame Selection module Adaptive Conditioner semantically-relevant global memory importance-guided patchification referential captions text-conditioned image-conditioned