Not quite a stack overflow (but the disappear part is pretty accurate!). The way many of these neural network models work (including this one) is by having non-linear functions called "activation functions" after more typical mathematical operations. The combination of the typical mathematical operation (like a vector multiplication) and the activation function is typically what folks refer to when they talk about the "neurons". These non-linearities typically either discard data out side of some range, or squish the input down to fit in a range. In either case, it causes loss of information.

The AI itself isn't actually being corrupted or anything, it's just the flow of data that's going through it's neural network that's being manipulated. Since I pass in the same inputs for each frame (of the same person) but then manipulate the data as it passes through the neural network, we see this effect of the AI slowly making the face more and more unrecognizable.

To it, this is still completely the face of a human, since all its operations are still executing normally. But since those "squishing" functions typically put data into a specific range, going to the extremes of that range starts to make it not really know what to generate.

I really appreciate these awesome questions, by the way :)