Existing methods for video interpolation heavily rely on deep convolution
neural networks, and thus suffer from their intrinsic limitations, such as
content-agnostic kernel weights and restricted receptive field. To address
these issues, we propose a Transformer-based video interpolation framework that
allows content-aware aggregation weights and considers long-range dependencies
with the self-attention operations. To avoid the high computational cost of
global self-attention, we introduce the concept of local attention into video
interpolation and extend it to the spatial-temporal domain. Furthermore, we
propose a space-time separation strategy to save memory usage, which also
improves performance. In addition, we develop a multi-scale frame synthesis
scheme to fully realize the potential of Transformers. Extensive experiments
demonstrate the proposed model performs favorably against the state-of-the-art
methods both quantitatively and qualitatively on a variety of benchmark
datasets.

本论文提出了一种基于 Transformer 模型的视频插帧方法，利用自注意力机制实现了内容关注的多尺度帧间聚合，并引入本地注意力机制以及时间与空间的分离策略以优化性能。实验证明该方法在多个基准数据集上得到了优于现有方法的表现。