Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN

Abstract

This paper presents a new deformable convolution based video frame interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the multi-flow prediction. This model first extracts spatio-temporal features at multiple scales using a 3D CNN, and estimates multi-flows using these features in a coarse-to-fine manner. The estimated multi-flows are then used to warp the original input frames as well as context maps, and the warped results are fused by a synthesis network to produce the final output. This VFI approach has been fully evaluated against 12 state-of-the-art VFI methods on three commonly used test databases. The results evidently show the effectiveness of the proposed method, which offers superior interpolation performance over other tested algorithms, with PSNR gains up to 0.19dB.

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Citation

@inproceedings{danier2022enhancing,
    title={Enhancing deformable convolution based video frame interpolation with coarse-to-fine 3d cnn},
    author={Danier, Duolikun and Zhang, Fan and Bull, David},
    booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
    pages={1396--1400},
    year={2022},
    organization={IEEE}
}