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Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation 

Authors: Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz

Affiliations: UMass Amherst, NVIDIA, UC Merced

*[http://openaccess.thecvf.com/content_cvpr_2018/html/Jiang_Super_SloMo_High_CVPR_2018_paper.html CVPR 2018 Paper][https://arxiv.org/abs/1712.00080 Arxiv Mirror]
*[https://github.com/avinashpaliwal/Super-SloMo Unofficial implementation by Avinash Paliwal]

==Method==
We're given two images, \(I_0\) and \(I_1\).  
The goal is to predict an intermediate image \(I_t\).  

First, estimate the optical flow \(F_{0\to 1}\) and \(F_{1 \to 0}\). This is done using an optical flow neural network.  
Then given these two, we can estimate the optical flow from the intermediate frame as follows:

*<math>\hat{F}_{t\to 0} = -(1-t)t F_{0 \to 1} + t^2 F_{1 \to 0}</math>
*<math>\hat{F}_{t \to 1} = (1-t)^2 F_{0 \to 1} - t(1-t)F_{1 \to 0}</math>
{{hidden | Derivation | 
We consider estimating <math display="inline">F_{t \to 1}(p)</math>.  
The time distance from t to 1 is (1-t).  
Thus if we estimate using <math display="inline">F_{0 \to 1}</math>, our estimate is <math>\frac{1-t}{1}F_{0 \to 1}(p)</math>.  
On the other hand, if we estimate using <math display="inline">F_{0 \to 1}</math>, the flow is backwards so our estimate is <math>-\frac{1-t}{1}F_{1 \to 0}</math>.  
When <math display="inline">t=0</math>, we want our estimate to be identical to <math display="inline">F_{0 \to 1}</math>. When <math display="inline">t=1</math>, we want our estimate to be equal to <math display="inline">F_{0 \to 1}</math>.  
Thus our final estimate is <math display="inline">\hat{F}_{t \to 1} = (1-t)(1-t)F_{0 \to 1} + (t)(1(1-t))F_{1 \to 0) = (1-t)^2 F_{0 \to 1} - t(1-t)F_{1 \to 0}</math>.

Derivation for <math display="inline">\hat{F}_{t \to 0}</math> is the same except the distance to <math display="inline">t=0</math> is <math display="inline">-t</math>.
}}

The estimate of the intermediate frame now is:

*<math>\hat{I}_t = \alpha_0 \odot g(I_0, F_{t \to 0}) + (1 - \alpha_0) \odot g(I_1, F_{t \to 1})</math>

where \(g\) is a differentiable backward warping function (bilinear interpolation) and \(\alpha_0\) controls the pixelwise contribution from each image.  
A naive estimate would use \(\alpha_0 = (1-t)\). However, to address occlusions, it is necessary to find the visibility maps.  

Visibility maps \(V_{t \leftarrow 0}\) and \(V_{t \leftarrow 1}\) tell us whether each pixel in \(I_t\) is visible in \(I_0\) and \(I_1\) respectively.  
These visibility maps are estimated using a flow-interpolation neural network.  
During training, they enforce \(V_{t \leftarrow 0} + V_{t \leftarrow 1} = 1\).

The final images estimate is:

*<math>\hat{I}_t = \frac{1}{Z} \odot \left( (1-t)V_{t \leftarrow 1} \odot g(I_0, F_{t \to 0}) + tV_{t \leftarrow 1} \odot g(I_1, F_{t \to 1}) \right)</math>

==Architecture==
Their architecture consists of two similar CNNS, a flow computation CNN to compute the bidirectional flow between the two images and a flow interpolation CNN.
Both networks are fully-convolution U-Net with 6 hierarchies in the encoder and 5 hierarchies in the decoder.

===Flow Computation CNN===

===Flow Interpolation CNN===

==Resources==
==References==