Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation

TL;DR

This paper introduces a texture consistency loss and a cross-scale pyramid alignment module to improve video frame interpolation, addressing motion ambiguity and computational efficiency issues in existing methods.

Contribution

It proposes a novel texture consistency loss and an efficient cross-scale pyramid alignment module to enhance VFI quality and speed, overcoming limitations of previous approaches.

Findings

Texture consistency loss improves interpolation quality.

CSPA module reduces computational complexity to O(N).

Experimental results show enhanced performance and efficiency.

Abstract

For video frame interpolation (VFI), existing deep-learning-based approaches strongly rely on the ground-truth (GT) intermediate frames, which sometimes ignore the non-unique nature of motion judging from the given adjacent frames. As a result, these methods tend to produce averaged solutions that are not clear enough. To alleviate this issue, we propose to relax the requirement of reconstructing an intermediate frame as close to the GT as possible. Towards this end, we develop a texture consistency loss (TCL) upon the assumption that the interpolated content should maintain similar structures with their counterparts in the given frames. Predictions satisfying this constraint are encouraged, though they may differ from the pre-defined GT. Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks. On the other hand, previous methods usually adopt the cost volume or correlation map to achieve more accurate image/feature warping. However, the O(N^2) ({N refers to the pixel count}) computational complexity makes it infeasible for high-resolution cases. In this work, we design a simple, efficient (O(N)) yet powerful cross-scale pyramid alignment (CSPA) module, where multi-scale information is highly exploited. Extensive experiments justify the efficiency and effectiveness of the proposed strategy.