  Regularization is used to avoid overfitting when training a neural network;
unfortunately, this reduces the attainable level of detail hindering the
ability to capture high-frequency information present in the training data.
Even though various approaches may be used to re-introduce high-frequency
detail, it typically does not match the training data and is often not time
coherent. In the case of network inferred cloth, these sentiments manifest
themselves via either a lack of detailed wrinkles or unnaturally appearing
and/or time incoherent surrogate wrinkles. Thus, we propose a general strategy
whereby high-frequency information is procedurally embedded into low-frequency
data so that when the latter is smeared out by the network the former still
retains its high-frequency detail. We illustrate this approach by learning
texture coordinates which when smeared do not in turn smear out the
high-frequency detail in the texture itself but merely smoothly distort it.
Notably, we prescribe perturbed texture coordinates that are subsequently used
to correct the over-smoothed appearance of inferred cloth, and correcting the
appearance from multiple camera views naturally recovers lost geometric
information.
