Video Frame Interpolation (VFI) is a crucial technique in various
applications such as slow-motion generation, frame rate conversion, video frame
restoration etc. This paper introduces an efficient video frame interpolation
framework that aims to strike a favorable balance between efficiency and
quality. Our framework follows a general paradigm consisting of a flow
estimator and a refinement module, while incorporating carefully designed
components. First of all, we adopt depth-wise convolution with large kernels in
the flow estimator that simultaneously reduces the parameters and enhances the
receptive field for encoding rich context and handling complex motion.
Secondly, diverging from a common design for the refinement module with a
UNet-structure (encoder-decoder structure), which we find redundant, our
decoder-only refinement module directly enhances the result from coarse to fine
features, offering a more efficient process. In addition, to address the
challenge of handling high-definition frames, we also introduce an innovative
HD-aware augmentation strategy during training, leading to consistent
enhancement on HD images. Extensive experiments are conducted on diverse
datasets, Vimeo90K, UCF101, Xiph and SNU-FILM. The results demonstrate that our
approach achieves state-of-the-art performance with clear improvement while
requiring much less FLOPs and parameters, reaching to a better spot for
balancing efficiency and quality.

本研究介绍了一种有效的视频帧插值框架，通过采用深度卷积和创新的高清增强策略，实现了对视频帧处理效能和质量的平衡。