Name
Title | ||
|---|---|---|
Predicting ready-made garment dressing fit for individuals based on highly reliable examples. |
Abstract | ||
|---|---|---|
•Generating highly reliable body-garment paired data by digitalizing the garment on a shape-changing robotic mannequin with the method of physically simulating the twin virtual garment with the guidance of vision.•The paired data of a template garment are generalized to be suitable to garments with similar style by scaling the template garment-body ease-allowance, i.e., the extra measurement added to the body measurement, for different sized human bodies.•Deformation characters of a garment template are transformed to daily garments to predict their dressing effects on different persons by training a CNN-based network, which can obtain plausible results. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cag.2020.06.002 | Computers & Graphics |
Keywords | DocType | Volume |
Virtual try-on,Robotic mannequin,Convolutional neural network,Clothes deformation | Journal | 90 |
ISSN | Citations | PageRank |
0097-8493 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haocan Xu | 1 | 1 | 1.38 |
| Jituo Li | 2 | 55 | 10.00 |
| Guodong Lu | 3 | 68 | 14.74 |
| Dong-Liang Zhang | 4 | 50 | 7.55 |
| Juncai Long | 5 | 0 | 0.34 |