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Related Concept Videos

Masking and Demasking Agents01:19

Masking and Demasking Agents

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Related Experiment Video

Updated: May 15, 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

MultiPaint: A Unified Framework for Multi-Task, Multi-Object, and Multi-Condition Video Inpainting.

Shiyuan Yang, Zheng Gu, Liang Hou

    IEEE Transactions on Visualization and Computer Graphics
    |May 13, 2026
    PubMed
    Summary

    MultiPaint unifies video inpainting for multiple objects and tasks, enabling both insertion and completion. This advanced framework improves multi-object interaction and appearance customization for better video editing.

    Related Experiment Videos

    Last Updated: May 15, 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

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing video inpainting methods struggle with unified support for insertion and completion tasks.
    • Current approaches are limited to single-object inpainting, hindering multi-object scenarios with interactions.

    Purpose of the Study:

    • To introduce MultiPaint, a unified framework for multi-task, multi-object, and multi-condition video inpainting.
    • To enhance controllability and performance in complex video editing scenarios.

    Main Methods:

    • Utilized dual-branch adapters to unify insertion and completion tasks within a single model.
    • Implemented a test-time scheduled feature composition strategy for multi-object inpainting with interaction preservation.
    • Introduced a multi-condition inpainting scheme (text, image, keyframe-guided) via dynamic frame masking.

    Main Results:

    • Achieved state-of-the-art performance in object insertion and scene completion tasks.
    • Demonstrated superior handling of multi-object scenarios and interactions.
    • Showcased versatility in downstream applications like grounded video generation and object editing.

    Conclusions:

    • MultiPaint offers a unified and versatile solution for advanced video inpainting.
    • The framework significantly improves upon existing methods for complex multi-object and multi-condition video editing.