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Video Frame Interpolation via Generalized Deformable Convolution
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DOI：10.1109/TMM.2021.3052419.png)
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Abstract
En 中文
Video frame interpolation aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies. The existing deep-learning-based video frame interpolation methods can be roughly divided into two categories: flow-based methods and kernel-based methods. The performance of flow-based methods is often jeopardized by the inaccuracy of flow map estimation due to oversimplified motion models, while that of kernel-based methods tends to be constrained by the rigidity of kernel shape. To address these performance-limiting issues, a novel mechanism named generalized deformable convolution is proposed, which can effectively learn motion information in a data-driven manner and freely select sampling points in space-time. We further develop a new video frame interpolation method based on this mechanism. Our extensive experiments demonstrate that the new method performs favorably against the state-of-the-art, especially when dealing with complex motions. Code is available at https://github.com/zhshi0816/GDConvNet.
Keywords:
Convolution
Interpolation
Kernel
Shape
Streaming media
Estimation
Task analysis
Generalized deformable convolution
video frame interpolation
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