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arXiv · 2001.07253

Recovering Geometric Information with Learned Texture Perturbations

Jane Wu·Yongxu Jin·Zhenglin Geng·Hui Zhou·Ronald Fedkiw
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Abstract

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.

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Jane Wu, Yongxu Jin, Zhenglin Geng, Hui Zhou, Ronald Fedkiw. 2020-01-20. Recovering Geometric Information with Learned Texture Perturbations. https://arxiv.org/abs/2001.07253

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