Name
Title | ||
|---|---|---|
Escher-Like Tiling Design From Video Images Using Convolutional Variational Autoencoder |
Abstract | ||
|---|---|---|
This paper proposes a method that deforms a prominent movie or animation character into a tileable shape. Tiling is the act of covering the plane with one or a very few types of figures without overlaps and/or gaps. Although some previous methods can transform a given shape into a tileable shape, they cannot easily move the character into a suitably tileable pose. The proposed method learns the latent feature space that abstracts the target character's silhouettes using a convolutional variational autoencoder, and looks for the poses suitable for tiling by optimization in the latent space. Experimental results showed that the proposed method successfully generated tileable figures of the tested character in various poses, some of which were not included in the training dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.6688/JISE.202105_37(3).0005 | JOURNAL OF INFORMATION SCIENCE AND ENGINEERING |
Keywords | DocType | Volume |
tiling, tessellations, hierarchical optimization, genetic algorithm, convolutional variational autoencoder | Journal | 37 |
Issue | ISSN | Citations |
3 | 1016-2364 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asuka Hisatomi | 1 | 0 | 0.34 |
| Tomofumi Matsuyama | 2 | 0 | 0.34 |
| Takahiro Kinoshita | 3 | 0 | 0.34 |
| Kazunori Mizuno | 4 | 0 | 0.34 |
| Satoshi Ono | 5 | 219 | 39.83 |