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Motion estimation (ME) and motion compensation (MC) have been widely used forclassical video frame interpolation systems over the past decades. Recently, anumber of data-driven frame interpolation methods based on convolutional neuralnetworks have been proposed. However, existing learning based methods typicallyestimate either flow or compensation kernels, thereby limiting performance onboth computational efficiency and interpolation accuracy. In this work, wepropose a motion estimation and compensation driven neural network for videoframe interpolation. A novel adaptive warping layer is developed to integrateboth optical flow and interpolation kernels to synthesize target frame pixels.This layer is fully differentiable such that both the flow and kernelestimation networks can be optimized jointly. The proposed model benefits fromthe advantages of motion estimation and compensation methods without usinghand-crafted features. Compared to existing methods, our approach iscomputationally efficient and able to generate more visually appealing results.Furthermore, the proposed MEMC-Net can be seamlessly adapted to several videoenhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensivequantitative and qualitative evaluations demonstrate that the proposed methodperforms favorably against the state-of-the-art video frame interpolation andenhancement algorithms on a wide range of datasets.

MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement