The paper you are citing is the paper that introduced the cascaded convolution neural network. A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems. A convolutional neural network (cnn) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer.
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Typically for a cnn architecture, in a single filter as described by your number_of_filters parameter, there is one 2d kernel per input channel. So, you cannot change dimensions like you mentioned. Equivalently, an fcn is a cnn.
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The concept of cnn itself is that you want to learn features from the spatial domain of the image which is xy dimension.
In fact, in this paper, the authors say to realize 3ddfa, we propose to combine two achievements in recent. What is your knowledge of rnns and cnns? There are input_channels * number_of_filters sets of. And then you do cnn part for 6th frame and you pass.
Fully convolution networks a fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations. Do you know what an lstm is? But if you have separate cnn to extract features, you can extract features for last 5 frames and then pass these features to rnn. You can use cnn on any data, but it's recommended to use cnn only on data that have spatial features (it might still work on data that doesn't have spatial features, see duttaa's comment below).
