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Related Experiment Video

Updated: Apr 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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UniVST: A Unified Framework for Training-Free Localized Video Style Transfer.

Quanjian Song, Mingbao Lin, Wengyi Zhan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 6, 2025
    PubMed
    Summary
    This summary is machine-generated.

    UniVST offers training-free localized video style transfer using diffusion models. This novel framework enhances temporal consistency and preserves details, outperforming existing methods for stylized video generation.

    Related Experiment Videos

    Last Updated: Apr 7, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.7K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing diffusion models for video style transfer often require training and struggle with preserving localized details and temporal consistency.
    • Direct video stylization methods can lead to loss of critical object details and temporal artifacts.

    Purpose of the Study:

    • To introduce UniVST, a unified, training-free framework for localized video style transfer using diffusion models.
    • To address the limitations of existing methods in maintaining content fidelity, style richness, and temporal consistency in stylized videos.

    Main Methods:

    • A point-matching mask propagation strategy utilizing DDIM inversion feature maps, eliminating the need for tracking models.
    • A training-free AdaIN-guided mechanism operating at latent and attention levels for balanced content and style.
    • A sliding-window consistent smoothing scheme incorporating optical flow for enhanced temporal consistency and artifact reduction.

    Main Results:

    • UniVST demonstrates superior performance over existing methods in both quantitative and qualitative evaluations.
    • The framework effectively preserves the style of the primary object while maintaining temporal consistency and fine details.
    • Achieved significant enhancement in temporal consistency and reduction of artifacts in stylized videos.

    Conclusions:

    • UniVST presents a novel and effective approach to localized video style transfer.
    • The training-free, unified framework offers a significant advantage for diffusion-based video stylization.
    • The method successfully balances style transfer with content preservation and temporal coherence.