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.

中文翻译：

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使用学习的纹理扰动恢复几何信息

正则化用于在训练神经网络时避免过拟合；不幸的是，这降低了可达到的细节水平，阻碍了捕获训练数据中存在的高频信息的能力。尽管可以使用各种方法来重新引入高频细节，但它通常与训练数据不匹配，并且通常在时间上不一致。在网络推断布料的情况下，这些情绪通过缺乏详细皱纹或不自然出现和/或时间不连贯的替代皱纹表现出来。因此，我们提出了一种通用策略，即在程序上将高频信息嵌入到低频数据中，这样当后者被网络抹掉时，前者仍保留其高频细节。我们通过学习纹理坐标来说明这种方法，当被涂抹时，纹理坐标不会反过来涂抹纹理本身的高频细节，而只是平滑地扭曲它。值得注意的是，我们规定了扰动的纹理坐标，随后用于校正推断布料的过度平滑外观，并且从多个相机视图校正外观自然恢复丢失的几何信息。