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Multiple Video Frame Interpolation via Enhanced Deformable Separable Convolution.

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    This summary is machine-generated.

    This study introduces enhanced deformable separable convolution (EDSC) for video frame interpolation. EDSC overcomes limitations of kernel-based methods, enabling generation of multiple in-between frames even with large motion.

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    Area of Science:

    • Computer Vision
    • Video Processing
    • Deep Learning

    Background:

    • Video frame interpolation is challenging, especially with large motion.
    • Existing kernel-based methods struggle with motion exceeding kernel size and arbitrary temporal positions.
    • Optical flow methods are computationally expensive.

    Purpose of the Study:

    • To propose a novel non-flow kernel-based approach for improved video frame interpolation.
    • To address limitations of existing methods regarding large motion and arbitrary temporal positions.
    • To enable generation of multiple in-between frames.

    Main Methods:

    • Introduced enhanced deformable separable convolution (EDSC).
    • EDSC estimates adaptive kernels, offsets, masks, and biases for non-local neighborhood information.
    • Utilized an extension of the coord-conv trick to involve intermediate time steps as control variables.

    Main Results:

    • EDSC successfully generates multiple in-between frames.
    • The method performs favorably against state-of-the-art approaches on various datasets.
    • Demonstrated capability to handle larger scene motion than pre-defined kernel sizes.

    Conclusions:

    • EDSC offers a robust and flexible solution for video frame interpolation.
    • The proposed method outperforms traditional kernel-based and flow-based techniques.
    • Code is available for public use, facilitating further research.