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Related Experiment Video

Updated: Mar 18, 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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MotionPrior: Exploring Efficient Learning of Motion Concepts for Few-Shot Video Generation.

Yaosi Hu, Chang Wen Chen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 16, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a cost-effective method for adaptive motion concept video generation. The approach learns motion priors from limited data, enabling smooth video creation with single or multiple motion concepts, improving generation freedom with a light training burden.

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    Last Updated: Mar 18, 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:

    • Diffusion models have advanced text-to-image generation but video generation remains resource-intensive.
    • Existing few-shot video generation methods are limited to single motion concepts.
    • There's a need for cost-effective video generation with improved flexibility and reduced data/resource demands.

    Purpose of the Study:

    • To develop a cost-effective video generation scheme capable of handling adaptive and multiple motion concepts.
    • To enhance generation freedom in video synthesis without a significant increase in training burden.
    • To leverage limited video data for learning motion priors.

    Main Methods:

    • A learnable bank for motion concepts was constructed.
    • A Dual-Semantic-guided Motion Attention module was proposed to extract motion elements using textual and visual guidance.
    • Lightweight motion injection layers, a temporal-aware noise prior, and inter-frame consistency constraints were employed.

    Main Results:

    • The proposed method successfully learns motion priors adaptively from small datasets.
    • Generated videos exhibit smooth motion and support single or multiple motion concepts.
    • Experimental results show superior performance compared to existing few-shot and some large-scale video generation models.

    Conclusions:

    • The developed scheme offers a cost-effective solution for flexible video generation.
    • It effectively integrates motion semantics with reduced parameters and computational cost.
    • The approach demonstrates the potential for improved generation freedom in text-to-video synthesis using limited resources.