LADDER: An Efficient Framework for Video Frame Interpolation

• URL: http://arxiv.org/abs/2404.11108v1
• Date: Wed, 17 Apr 2024 06:47:17 GMT
• Title: LADDER: An Efficient Framework for Video Frame Interpolation
• Authors: Tong Shen, Dong Li, Ziheng Gao, Lu Tian, Emad Barsoum,
• Abstract summary: 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 framework that aims to strike a favorable balance between efficiency and quality.
• Score: 12.039193291203492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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