Learning Spatial Transform for Video Frame Interpolation
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Video frame interpolation is one of the most challenging tasks in the video processing area. Recently, many related studies based on deep learning have been suggested, which can be categorized into kernel estimation and flow map estimation approaches. Most of the methods focus on finding the locations with useful information to estimate each output pixel since the information needed to estimate an intermediate frame is fully contained in the two adjacent frames. However, we redefine the task as finding the spatial transform between adjacent frames and propose a new neural network architecture that combines the two abovementioned approaches, namely Adaptive Deformable Convolution. Our method is able to estimate both kernel weights and offset vectors for each output pixel, and the output frame is synthesized by the deformable convolution operation. The experimental results show that our method outperforms the state-of-the-art methods on several datasets and that our proposed approach contributes to performance enhancement.
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