Shape part Transfer via semantic latent space factorization
Groscot, Raphaël; Cohen, Laurent D.; Guibas, Leonidas (2019), Shape part Transfer via semantic latent space factorization, Geometric Science of Information 4th International Conference, GSI 2019, Toulouse, France, August 27–29, 2019, Proceedings, Springer : Berlin Heidelberg, p. 511-519. 10.1007/978-3-030-26980-7_53
Type
Communication / ConférenceDate
2019Conference country
FRANCEBook title
Geometric Science of Information 4th International Conference, GSI 2019, Toulouse, France, August 27–29, 2019, Proceedings; Proc. 4th conference on Geometric Science of Information (GSI2019)Publisher
Springer
Published in
Berlin Heidelberg
ISBN
Print ISBN 978-3-030-26979-1
Pages
511-519
Publication identifier
Metadata
Show full item recordAuthor(s)
Groscot, RaphaëlCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Cohen, Laurent D.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Guibas, Leonidas
Abstract (EN)
We present a latent space factorization that controls a generative neural network for shapes in a semantic way. Our method uses the segmentation data present in a collection of shapes to explicitly factorize the encoder of a pointcloud autoencoder network, replacing it by several sub-encoders. This allows to learn a semantically-structured latent space in which we can uncover statistical modes corresponding to semantically similar shapes, as well as mixing parts from several objects to create hybrids and quickly explore design ideas through varying shape combinations. Our work differs from existing methods in two ways: first, it proves the usefulness of neural networks to achieve shape combinations and second, adapts the whole geometry of the object to accommodate for its different parts.Subjects / Keywords
Autoencoder; Pointcloud; Latent spaceRelated items
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