StoryWeaver: A Unified World Model for Knowledge-Enhanced Story Character Customization

• URL: http://arxiv.org/abs/2412.07375v2
• Date: Tue, 17 Dec 2024 01:06:09 GMT
• Title: StoryWeaver: A Unified World Model for Knowledge-Enhanced Story Character Customization
• Authors: Jinlu Zhang, Jiji Tang, Rongsheng Zhang, Tangjie Lv, Xiaoshuai Sun,
• Abstract summary: We propose a novel knowledge graph, namely Character Graph (textbfCG), which comprehensively represents various story-related knowledge. We then introduce StoryWeaver, an image generator that achieve Customization via Character Graph (textbfC-CG), capable of consistent story visualization with rich text semantics.
• Score: 36.14275850149665
• License:
• Abstract: Story visualization has gained increasing attention in artificial intelligence. However, existing methods still struggle with maintaining a balance between character identity preservation and text-semantics alignment, largely due to a lack of detailed semantic modeling of the story scene. To tackle this challenge, we propose a novel knowledge graph, namely Character Graph (\textbf{CG}), which comprehensively represents various story-related knowledge, including the characters, the attributes related to characters, and the relationship between characters. We then introduce StoryWeaver, an image generator that achieve Customization via Character Graph (\textbf{C-CG}), capable of consistent story visualization with rich text semantics. To further improve the multi-character generation performance, we incorporate knowledge-enhanced spatial guidance (\textbf{KE-SG}) into StoryWeaver to precisely inject character semantics into generation. To validate the effectiveness of our proposed method, extensive experiments are conducted using a new benchmark called TBC-Bench. The experiments confirm that our StoryWeaver excels not only in creating vivid visual story plots but also in accurately conveying character identities across various scenarios with considerable storage efficiency, \emph{e.g.}, achieving an average increase of +9.03\% DINO-I and +13.44\% CLIP-T. Furthermore, ablation experiments are conducted to verify the superiority of the proposed module. Codes and datasets are released at https://github.com/Aria-Zhangjl/StoryWeaver.

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