论文标题
视频框架插值的实时中间流量估计
Real-Time Intermediate Flow Estimation for Video Frame Interpolation
论文作者
论文摘要
实时视频框架插值（VFI）在视频处理，媒体播放器和显示器方面非常有用。我们提出了RIFE，这是VFI的实时中间流量估计算法。为了实现高质量的基于流动的VFI方法，RIFE使用名为IFNET的神经网络，该网络可以以更快的速度端到端估算中间流。特权蒸馏计划是为稳定的IFNET培训而设计的，并改善了整体性能。 RIFE不依赖于预先训练的光流模型，并且可以通过时间编码输入来支持任意的临时框架插值。实验表明，Rife在几个公共基准上实现了最先进的表现。与流行的Superslomo和Dain方法相比，RIFE的速度更快4--27倍，并产生更好的结果。此外，由于时间编码，可以将RIFE扩展到更广泛的应用程序。该代码可从https://github.com/megvii-research/eccv2022-rife获得。
Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4--27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. The code is available at https://github.com/megvii-research/ECCV2022-RIFE.
