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Make-A-Storyboard: A General Framework for Storyboard with Disentangled and Merged Control

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arxiv 2312.07549 v1 pith:23ERBYWP submitted 2023-12-06 cs.CV

classification cs.CV
keywords storycharactersstoryboardconsistencyscenescenesimagesvisual
verification ladder T0 review T1 audit T2 compute T3 formal

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Story Visualization aims to generate images aligned with story prompts, reflecting the coherence of storybooks through visual consistency among characters and scenes.Whereas current approaches exclusively concentrate on characters and neglect the visual consistency among contextually correlated scenes, resulting in independent character images without inter-image coherence.To tackle this issue, we propose a new presentation form for Story Visualization called Storyboard, inspired by film-making, as illustrated in Fig.1.Specifically, a Storyboard unfolds a story into visual representations scene by scene. Within each scene in Storyboard, characters engage in activities at the same location, necessitating both visually consistent scenes and characters.For Storyboard, we design a general framework coined as Make-A-Storyboard that applies disentangled control over the consistency of contextual correlated characters and scenes and then merge them to form harmonized images.Extensive experiments demonstrate 1) Effectiveness.the effectiveness of the method in story alignment, character consistency, and scene correlation; 2) Generalization. Our method could be seamlessly integrated into mainstream Image Customization methods, empowering them with the capability of story visualization.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. StorySync: Training-Free Subject Consistency in Text-to-Image Generation via Region Harmonization

    cs.CV 2025-07 unverdicted novelty 5.0 of 10

    A training-free inference-time pipeline uses masked cross-image attention sharing and region harmonization to keep subjects consistent across generated story images.

  2. Text to Image Generation and Editing: A Survey

    cs.CV 2025-05 conditional novelty 3.0 of 10

    A broad survey of text-to-image generation and editing research from 2021 to 2024, organized by architecture and comparison tables.

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