DreamShot: 基于视频扩散先验的个性化分镜合成
DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior
AI总结：
DreamShot通过视频扩散先验实现可控的多镜头合成，支持文本到镜头和参考到镜头生成，提升叙事连贯性和角色一致性。
摘要：
分镜合成在视觉叙事中起关键作用，旨在生成视觉上讲述电影事件的连贯镜头序列。然而，现有方法多基于文本到图像扩散模型，难以维持长时间域时间一致性、一致的角色身份和叙事流程。本文提出DreamShot，一种基于分镜框架的视频生成模型，充分利用强大的视频扩散先验进行可控多镜头合成。DreamShot支持文本到镜头和参考到镜头生成，以及基于先前帧的故事情节延续，实现灵活且上下文感知的分镜生成。通过利用视频生成模型中固有的空间-时间一致性，DreamShot生成视觉和语义上连贯的序列，具有改进的叙事保真度和角色连续性。此外，DreamShot集成了多参考角色条件模块，接受多个角色参考图像并通过角色注意力一致性损失强制身份对齐，明确约束参考与生成角色之间的注意力。大量实验表明，DreamShot在场景一致性、角色一致性和生成效率方面优于最先进的文本到图像分镜模型，为可控视频模型驱动的视觉叙事开辟了新方向。
英文摘要：
Storyboard synthesis plays a crucial role in visual storytelling, aiming to generate coherent shot sequences that visually narrate cinematic events with consistent characters, scenes, and transitions. However, existing approaches are mostly adapted from text-to-image diffusion models, which struggle to maintain long-range temporal coherence, consistent character identities, and narrative flow across multiple shots. In this paper, we introduce DreamShot, a video generative model based storyboard framework that fully exploits powerful video diffusion priors for controllable multi-shot synthesis. DreamShot supports both Text-to-Shot and Reference-to-Shot generation, as well as story continuation conditioned on previous frames, enabling flexible and context-aware storyboard generation. By leveraging the spatial-temporal consistency inherent in video generative models, DreamShot produces visually and semantically coherent sequences with improved narrative fidelity and character continuity. Furthermore, DreamShot incorporates a multi-reference role conditioning module that accepts multiple character reference images and enforces identity alignment via a Role-Attention Consistency Loss, explicitly constraining attention between reference and generated roles. Extensive experiments demonstrate that DreamShot achieves superior scene coherence, role consistency, and generation efficiency compared to state-of-the-art text-to-image storyboard models, establishing a new direction toward controllable video model-driven visual storytelling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
