TY  - JOUR
TI  - String-Based Synthesis of Structured Shapes
EP  - 36
AV  - public
Y1  - 2019/05//
ID  - discovery10083155
N2  - We propose a novel method to synthesize geometric models from a given class of context?aware structured shapes such as buildings and other man?made objects. The central idea is to leverage powerful machine learning methods from the area of natural language processing for this task. To this end, we propose a technique that maps shapes to strings and vice versa, through an intermediate shape graph representation. We then convert procedurally generated shape repositories into text databases that, in turn, can be used to train a variational autoencoder. The autoencoder enables higher level shape manipulation and synthesis like, for example, interpolation and sampling via its continuous latent space. We provide project code and pre?trained models.
PB  - WILEY
VL  - 38
SP  - 27
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
IS  - 2
SN  - 1467-8659
UR  - https://doi.org/10.1111/cgf.13616
JF  - Computer Graphics Forum
A1  - Kalojanov, J
A1  - Lim, I
A1  - Mitra, N
A1  - Kobbelt, L
ER  -