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Updated: Nov 16, 2025

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Video Vectorization via Bipartite Diffusion Curves Propagation and Optimization.

Yuanqi Li, Chuan Wang, Jing Hong

    IEEE Transactions on Visualization and Computer Graphics
    |February 23, 2021
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    We developed a new video vectorization method using bipartite diffusion curves (BDCs) to convert raster videos into vector formats. This approach offers resolution independence, compact storage, and preserves fine details with high spatial-temporal coherence.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computer Graphics

    Background:

    • Raster videos consume significant storage and lose quality when scaled.
    • Existing vectorization methods struggle to preserve fine details and temporal coherence.

    Purpose of the Study:

    • To introduce a novel video vectorization approach for resolution-independent and compact video representation.
    • To preserve both sharp boundaries and smooth color variations in videos.

    Main Methods:

    • Classifying curves into salient and non-salient categories.
    • Developing a bipartite diffusion curves (BDCs) representation.
    • Propagating non-salient curves and optimizing salient curves across frames.

    Main Results:

    • Achieved low reconstruction errors and computational cost.
    • Preserved fine details within and across frames with high spatial-temporal coherence.
    • Demonstrated superior performance compared to state-of-the-art methods.

    Conclusions:

    • The proposed BDCs method effectively converts raster videos to vector format.
    • The approach offers benefits of resolution independence, compact storage, and detail preservation.
    • The method shows potential for video upsampling, comparable to super-resolution techniques.