PDWN: Pyramid Deformable Warping Network for Video Interpolation

TL;DR

This paper introduces PDWN, a pyramid deformable warping network for video interpolation that improves accuracy and efficiency by using coarse-to-fine deformable convolution offsets and cost volumes, with extensions for multiple input frames.

Contribution

The paper proposes a novel pyramid deformable warping network that enhances video interpolation accuracy and efficiency, and extends it to utilize four input frames for better results.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Reduces model size and inference time significantly.

Extends framework to four input frames with notable improvements.

Abstract

Video interpolation aims to generate a non-existent intermediate frame given the past and future frames. Many state-of-the-art methods achieve promising results by estimating the optical flow between the known frames and then generating the backward flows between the middle frame and the known frames. However, these methods usually suffer from the inaccuracy of estimated optical flows and require additional models or information to compensate for flow estimation errors. Following the recent development in using deformable convolution (DConv) for video interpolation, we propose a light but effective model, called Pyramid Deformable Warping Network (PDWN). PDWN uses a pyramid structure to generate DConv offsets of the unknown middle frame with respect to the known frames through coarse-to-fine successive refinements. Cost volumes between warped features are calculated at every pyramid level to help the offset inference. At the finest scale, the two warped frames are adaptively blended to generate the middle frame. Lastly, a context enhancement network further enhances the contextual detail of the final output. Ablation studies demonstrate the effectiveness of the coarse-to-fine offset refinement, cost volumes, and DConv. Our method achieves better or on-par accuracy compared to state-of-the-art models on multiple datasets while the number of model parameters and the inference time are substantially less than previous models. Moreover, we present an extension of the proposed framework to use four input frames, which can achieve significant improvement over using only two input frames, with only a slight increase in the model size and inference time.