We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the interpolated content feature. Second, we derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately. Combining these two designs enables us to generate promising task-oriented flows and reduce the difficulties in modeling large motions and handling occluded areas during frame interpolation. These qualities promote our model to achieve state-of-the-art performance on various benchmarks with high efficiency. Moreover, our convolution-based model competes favorably compared to Transformer-based models in terms of accuracy and efficiency. Our code is available at https://github.com/MCG-NKU/AMT.

翻译：我们提出全对多场变换（AMT），一种用于视频帧插值的新型网络架构。该架构基于两个核心设计。首先，我们构建所有像素对的双向相关体积，并利用预测的双向流检索相关性，以同时更新光流和插值内容特征。其次，我们从一对更新的粗粒度流中派生多组细粒度流场，分别对输入帧执行反向扭曲。结合这两项设计，我们能够生成面向任务的高质量光流，并降低帧插值中处理大运动与遮挡区域的难度。这些特性使我们的模型在多个基准测试中实现高效且最先进的性能。此外，基于卷积的模型在精度与效率上优于基于Transformer的模型。我们的代码开源在https://github.com/MCG-NKU/AMT。