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I'd like to understand how to determine the parameter of conditional gaussian distribution. Following is the network architecture of VUNET which learns the conditional gaussian distribution $q(z|x, \hat y)$ of appearance of human($z$) upon given two conditional inputs skeleton of body pose and groundTruth image($x$) of 128x128 resolution image

vunet architecture

I don't understand how $2\times2\times256$ vector and $1\times1$ vector can characterize $q(z|x, \hat y)$. Any advice to understand this point clearly?

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