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Video frame interpolation is a challenging task in the video processing field. Benefiting from the development of deep learning, many video frame interpolation methods have been proposed, which focus on sampling pixels with useful information to synthesize each output pixel using their own sampling operation. However, these works have data redundancy limitations and fail to sample the correct pixel of complex motions. To solve these problems, we propose a new warping framework to sample called multi-scale expandable deformable convolution(MSEConv) which employs a deep fully convolutional neural network to estimate multiple small-scale kernel weights with different expansion degrees and adaptive weight allocation for each pixel synthesis. MSEConv covers most prevailing research methods as special cases of it, thus MSEConv is also possible to be transferred to existing works for performance …