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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion.

Huiguo He, Huan Yang, Zixi Tuo

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary

    DreamStory generates consistent multi-subject story visualizations using large language models (LLMs) and a novel diffusion model. This framework ensures subject appearance and attribute consistency across generated scenes.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Current story visualization methods struggle with subject consistency in generated image sequences.
    • Diffusion models show promise but lack coherence for multi-subject narratives.
    • Automatic, open-domain story visualization remains a significant challenge.

    Purpose of the Study:

    • To introduce DreamStory, an automatic framework for generating coherent, multi-subject story visualizations.
    • To address the challenge of maintaining subject consistency across image sequences derived from textual narratives.
    • To develop a novel multi-subject consistent diffusion model for enhanced visual storytelling.

    Main Methods:

    • Leveraging large language models (LLMs) as story directors to generate descriptive prompts and annotate subjects.
    • Utilizing a novel Multi-Subject consistent Diffusion model (MSD) incorporating Masked Mutual Self-Attention (MMSA) and Masked Mutual Cross-Attention (MMCA).
    • Employing multimodal anchors (subject portraits and textual descriptions) to guide the MSD for consistent multi-subject generation.

    Main Results:

    • DreamStory successfully generates visually compelling and coherent story visualizations with consistent multi-subject representation.
    • The MSD, with MMSA and MMCA, effectively ensures detailed appearance and semantic consistency of subjects.
    • A new benchmark, DS-500, was established to evaluate story visualization framework performance, subject identification, and generation consistency.

    Conclusions:

    • DreamStory provides an effective solution for automatic, open-domain story visualization with improved multi-subject consistency.
    • The proposed MSD architecture significantly enhances the quality and coherence of visual storytelling from text.
    • The DS-500 benchmark facilitates future research and evaluation in the domain of story visualization.