Frame interpolation is the process of synthesising a new frame in-between existing frames in an image sequence. It has emerged as a key algorithmic module in motion picture effects. In the context of this special issue, this study provides a review of the technology used to create in-between frames and presents a Bayesian framework that generalises frame interpolation algorithms using the concept of motion interpolation. Unlike existing literature in this area, the authors also compare performance using the top industrial toolkits used in the post production industry. They find that all successful techniques employ motion-based interpolation, and the commercial version of the Bayesian approach performs best. Another goal of this study is to compare the performance gains with recent convolutional neural network (CNN) algorithms against the traditional explicit model-based approaches. They find that CNNs do not clearly outperform the explicit motion-based techniques, and require significant compute resources, but provide complementary improvements in certain types of sequences.

中文翻译：

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影视效果的基于运动的帧插值

帧插值是在图像序列中现有帧之间合成新帧的过程。它已成为动态影像效果中的关键算法模块。在此特殊问题的背景下，本研究对用于创建中间帧的技术进行了回顾，并提出了一种贝叶斯框架，该框架使用运动插值的概念概括了帧插值算法。与该领域的现有文献不同，作者还使用后期制作行业中使用的顶级工业工具包来比较性能。他们发现，所有成功的技术都采用基于运动的插值，而贝叶斯方法的商业版本效果最佳。这项研究的另一个目标是将使用最新的卷积神经网络（CNN）算法与传统的基于显式模型的方法的性能提升进行比较。他们发现，CNN不能明显胜过基于运动的显式技术，并且需要大量的计算资源，但可以对某些类型的序列提供补充性的改进。