Recovering Geometric Information with Learned Texture Perturbations

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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
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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@inproceedings{
10.1145:3480137
, booktitle = {
Proceedings of the ACM on Computer Graphics and Interactive Techniques
}, editor = {
Narain, Rahul and Neff, Michael and Zordan, Victor
}, title = {{
Recovering Geometric Information with Learned Texture Perturbations
}}, author = {
Wu, Jane
and
Jin, Yongxu
and
Geng, Zhenglin
and
Zhou, Hui
and
Fedkiw, Ronald
}, year = {
2021
}, publisher = {
ACM
}, ISSN = {
2577-6193
}, ISBN = {}, DOI = {
10.1145/3480137
} }
Citation