Patent ID: 11952693
Assignee: LEVI STRAUSS & CO.
Field: Other consumer goods (Other fields)
Classification: CPC D  A  B  G | IPC A  B  D  G

Claim 18:
19. A method comprising:
providing an assembled garment made from fabric panels of a first material comprising a warp comprising indigo ring-dyed cotton yarn, wherein the fabric panels are sewn together using threads;
providing a laser input file that is representative of an existing finishing pattern from an existing garment made from a second material having a second fabric characteristic that is different from a first fabric characteristic of the first material, wherein the existing finishing pattern on the existing garment was not created by a laser, and the laser input file was obtained at least by a trained generative adversarial network, and a generative adversarial network is trained into the trained generative adversarial network at least by:
providing real laser input files and images of sample garments that have lased finishing patterns resulting from the real laser input files as a training dataset to the generative adversarial network, wherein at least one image of the images of the sample garments captures at least a lased finishing pattern of the lased finishing patterns on a sample garment of the sample garments, and a real laser input file of the real laser input files is used to control the laser that lases the lased finishing pattern onto the sample garment,
using a generative neural network of the generative adversarial network, generating fake laser input files for the images of the sample garments with the lased finishing patterns resulting from the real laser input files;
determining a generator loss based at least in part upon the fake laser input files and the real laser input files both of which are provided to determine the generator loss;
inputting at least the real laser input files to a real discriminator of the generative adversarial network;
inputting the real laser input files and the fake laser input files to a fake discriminator of the generative adversarial network;
determining a discriminator loss based at least in part upon respective outputs of the real discriminator and fake discriminator; and
based at least in part on the generator loss and the discriminator loss, iteratively modifying the generative adversarial network into the trained generative adversarial network, wherein the trained generative adversarial network generates the laser input file for an image of the existing garment with the finishing pattern that is not created by the laser;
using the laser to create a lased finishing pattern on an outer surface of the assembled garment based at least in part upon the laser input file, wherein the laser removes selected amounts of material from the surface of the first material at different pixel locations of the assembled garment based at least in part upon on the laser input file,
for lighter pixel locations of the lased finishing pattern, a greater amount of the indigo ring-dyed cotton yarn is removed, while for darker pixel locations of the lased finishing pattern, a lesser amount of the indigo ring-dyed cotton yarn is removed; and
the lased finishing pattern created is able to extend across portions of the assembled garment where two or more fabric panels are joined together by threads at least by exposing these portions to the laser.