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ACM Comput. Graph. Interact. Tech."],"published-print":{"date-parts":[[2021,9,22]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3480137","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T04:43:36Z","timestamp":1632804216000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Recovering Geometric Information with Learned Texture Perturbations"],"prefix":"10.1145","volume":"4","author":[{"given":"Jane","family":"Wu","sequence":"first","affiliation":[{"name":"Stanford University, USA"}]},{"given":"Yongxu","family":"Jin","sequence":"additional","affiliation":[{"name":"Stanford University, USA"}]},{"given":"Zhenglin","family":"Geng","sequence":"additional","affiliation":[{"name":"Epic Games, USA"}]},{"given":"Hui","family":"Zhou","sequence":"additional","affiliation":[{"name":"JD.com, USA"}]},{"given":"Ronald","family":"Fedkiw","sequence":"additional","affiliation":[{"name":"Stanford University, Epic Games, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"[n.d.]. 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