Video Frame Interpolation

IFBlock is a video model block used in the IFNet architecture for video frame interpolation. IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 convolution and deconvolution as building blocks. Each IFBlock has a feed-forward structure consisting of several convolutional layers and an upsampling operator. Except for the layer that outputs the optical flow residuals and the fusion map, PReLU activations are used.

IFNet is an architecture for video frame interpolation that adopts a coarse-to-fine strategy with progressively increased resolutions: it iteratively updates intermediate flows and soft fusion mask via successive IFBlocks. Conceptually, according to the iteratively updated flow fields, we can move corresponding pixels from two input frames to the same location in a latent intermediate frame and use a fusion mask to combine pixels from two input frames. Unlike most previous optical flow models, IFBlocks do not contain expensive operators like cost volume or forward warping and use 3 × 3 convolution and deconvolution as building blocks.

RIFE, or Real-time Intermediate Flow Estimation is an intermediate flow estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boundaries. RIFE uses a neural network named IFNet that can directly estimate the intermediate flows from coarse-to-fine with much better speed. It introduces a privileged distillation scheme for training intermediate flow model, which leads to a large performance improvement. In RIFE training, given two input frames $I_{0}, I_{1}$, we directly feed them into the IFNet to approximate intermediate flows $F_{t \rightarrow 0}, F_{t \rightarrow 1}$ and the fusion map $M$. During training phase, a privileged teacher refines student's results to get $F_{t \rightarrow 0}^{T e a}, F_{t \rightarrow 1}^{T e a}$ and $M^{\text {Tea }}$ based on ground truth $I_{t}$. The student model and the teacher model are jointly trained from scratch using the reconstruction loss. The teacher's approximations are more accurate so that they can guide the student to learn.