Please check our Report for more details.
Given segmentation json
along with its corresponding image
, it is possible to extract only garment geometries from the ICON's full reconstruction.
Format of segmentation json
:
{
"item1": {
"segmentation": [
[
x1,y1,x2,y2,.......
],
[
.....
]
],
"category_id": 0,
"category_name": "{clothing description}"
},
"item2": {
"segmentation": [
[
x1,y1,x2,y2,.......
],
[
.....
]
],
"category_id": 0,
"category_name": "{clothing description}"
}
}
Where "x1, y1, ..." are the coordinates of the segmentation boundaries. "category_id" identifies the type of clothing (refer to DeepFashion2). The segmentation and image files must have the same name. Please check a few samples under the examples/segmentation
.
To run ICON with segmentation you need to add -seg_dir {path_to_segmentation_folder}
, all the output results are loccated under {out_dir}/{method}/clothes
.
# Inference with segmentation
python -m apps.infer -cfg ./configs/icon-filter.yaml -gpu 0 -in_dir ./examples/segmentation -out_dir ./results -seg_dir ./examples/segmentation
# output under results/icon-filter/clothes
@techreport{Gudmundsson2022garment,
title = "3D Garments Crawler from large-scale fashion images",
author = "Gudmundsson, Daniel and Barrau-Joyeaux, Marion and Collette, Arthur and Kjaer, Amalie and Peng, Songyou and Xiu, Yuliang",
institution = "ETH Zürich",
year = 2022,
month = jun
}
For questions, please contact with Daniel Gudmundsson, Marion Barrau-Joyeaux, Arthur Collette, and Amalie Kjaer.