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LIA: Latent Image Animator.

Yaohui Wang, Di Yang, Francois Bremond

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 23, 2024
    PubMed
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    The Latent Image Animator (LIA) simplifies image animation by using a self-supervised autoencoder, avoiding complex structures. This method achieves superior video quality and consistency for high-resolution animations.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Traditional image animation methods rely on explicit structure representations like meshes or keypoints.
    • These methods struggle with significant appearance variations and require complex modules for appearance and motion modeling.

    Purpose of the Study:

    • To introduce a streamlined autoencoder-based approach for animating high-resolution images.
    • To address limitations of existing methods by avoiding explicit structure representations and complex modules.

    Main Methods:

    • Developed the Latent Image Animator (LIA), a simple autoencoder that operates in the latent space.
    • Modeled motion transfer as linear navigation of motion codes within the latent space.
    • Introduced Linear Motion Decomposition (LMD) for self-supervised learning of an orthogonal motion dictionary.

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    Main Results:

    • LIA demonstrates superior performance on VoxCeleb, TaichiHD, and TED-talk datasets.
    • Achieved state-of-the-art results in terms of video quality and spatio-temporal consistency.
    • Showcased effective zero-shot high-resolution image animation capabilities.

    Conclusions:

    • LIA offers an effective and simplified approach to high-resolution image animation.
    • The method overcomes challenges related to appearance variations and complex modeling.
    • LIA provides a robust solution for realistic and consistent video generation from static images.