DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior

• URL: http://arxiv.org/abs/2604.17195v1
• Date: Sun, 19 Apr 2026 01:51:41 GMT
• Title: DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior
• Abstract summary: We introduce DreamShot, a video generative model based storyboard framework.<n>DreamShot exploits powerful video diffusion priors for controllable multi-shot synthesis.<n>It produces visually and semantically coherent sequences with improved narrative fidelity and character continuity.
• Score: 84.56343116275423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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