We present a frame interpolation algorithm that synthesizes multiple
intermediate frames from two input images with large in-between motion. Recent
methods use multiple networks to estimate optical flow or depth and a separate
network dedicated to frame synthesis. This is often complex and requires scarce
optical flow or depth ground-truth. In this work, we present a single unified
network, distinguished by a multi-scale feature extractor that shares weights
at all scales, and is trainable from frames alone. To synthesize crisp and
pleasing frames, we propose to optimize our network with the Gram matrix loss
that measures the correlation difference between feature maps. Our approach
outperforms state-of-the-art methods on the Xiph large motion benchmark. We
also achieve higher scores on Vimeo-90K, Middlebury and UCF101, when comparing
to methods that use perceptual losses. We study the effect of weight sharing
and of training with datasets of increasing motion range. Finally, we
demonstrate our model's effectiveness in synthesizing high quality and
temporally coherent videos on a challenging near-duplicate photos dataset.
Codes and pre-trained models are available at this https URL

本文提出了一种针对大幅度中间动作的帧内插法算法，采用多尺度特征提取器的单一统一网络来进行训练，通过优化 Gram 矩阵损失实现了高质量的视频帧合成，竞争性地超越其他基于感知损失的方法，在多个基准测试中获得了更高的分数，并通过实验证明了该模型在难度较大的近似照片数据集上的有效性。