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NADM: Noise-Aware Diffusion Model for Landscape Painting Video Generation.

Ding-Ming Liu, Shao-Wei Li, Ruo-Yan Zhou

    IEEE Transactions on Cybernetics
    |June 24, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new dataset and a noise-aware diffusion model (NADM) for generating dynamic landscape painting videos. The approach enhances artistic video generation by capturing aesthetic dynamism and smooth transitions.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Digital Art

    Background:

    • Traditional landscape paintings are static, limiting the viewer's imagination of dynamic scenes.
    • Emerging text-to-video (T2V) models generate natural videos but struggle with artistic aesthetics and specific datasets.
    • Generating high-quality, dynamic landscape painting videos presents unique challenges due to style intricacy and data scarcity.

    Purpose of the Study:

    • To develop a novel text-to-video (T2V) dataset specifically for landscape painting videos.
    • To propose a new T2V model, the noise-aware diffusion model (NADM), for generating dynamic and aesthetically pleasing landscape painting videos.
    • To address the limitations of current T2V methods in capturing the dynamic aesthetic of artistic videos.

    Main Methods:

    • Introduced the landscape painting videos-high definition (LPV-HD) dataset.
    • Developed the noise-aware diffusion model (NADM) based on Stable Diffusion.
    • Implemented a motion module with a dual attention mechanism for dynamic image transformations.
    • Utilized a noise adapter with unsupervised contrastive learning for latent space beauty enhancement.
    • Employed optical flow for frame interpolation to improve video smoothness.

    Main Results:

    • The proposed NADM successfully generates dynamic landscape painting videos that retain the essence of the original artworks.
    • The dual attention mechanism effectively captures dynamic transformations in landscape imagery.
    • The noise adapter and frame interpolation contribute to overall aesthetic quality and video smoothness.
    • The LPV-HD dataset provides a valuable resource for future research in artistic video generation.

    Conclusions:

    • The NADM and LPV-HD dataset represent a significant advancement in generating dynamic artistic videos.
    • The method successfully balances the preservation of artistic essence with the creation of dynamic visual experiences.
    • This work opens new possibilities for digital art creation and the animation of traditional artworks.