US 11,907,839 B2
Detail-preserving image editing techniques
Ratheesh Kalarot, San Jose, CA (US); Kevin Wampler, Seattle, WA (US); Jingwan Lu, Santa Clara, CA (US); Jakub Fiser, Milton Keynes (GB); Elya Shechtman, Seattle, WA (US); Aliakbar Darabi, Seattle, WA (US); and Alexandru Vasile Costin, Monte Sereno, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Sep. 7, 2021, as Appl. No. 17/468,511.
Claims priority of provisional application 63/092,980, filed on Oct. 16, 2020.
Prior Publication US 2022/0122307 A1, Apr. 21, 2022
Int. Cl. G06N 3/08 (2023.01); G06F 3/04845 (2022.01); G06T 11/60 (2006.01); G06T 3/40 (2006.01); G06T 3/00 (2006.01); G06F 3/04847 (2022.01); G06N 20/20 (2019.01); G06T 5/00 (2006.01); G06T 5/20 (2006.01); G06T 11/00 (2006.01); G06F 18/40 (2023.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01)
CPC G06N 3/08 (2013.01) [G06F 3/04845 (2013.01); G06F 3/04847 (2013.01); G06F 18/211 (2023.01); G06F 18/214 (2023.01); G06F 18/2163 (2023.01); G06F 18/40 (2023.01); G06N 3/045 (2023.01); G06N 20/20 (2019.01); G06T 3/0006 (2013.01); G06T 3/0093 (2013.01); G06T 3/40 (2013.01); G06T 3/4038 (2013.01); G06T 3/4046 (2013.01); G06T 5/005 (2013.01); G06T 5/20 (2013.01); G06T 11/001 (2013.01); G06T 11/60 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2210/22 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
providing, by a computing system, an input image as input to a machine learning model to generate a latent space representation of the input image;
providing, by the computing system, the latent space representation of the input image as input to a trained generator neural network implemented by the computing system;
generating, by the generator neural network, a generated image based on the latent space representation of the input image;
generating, by the computing system, a first scale representation of the input image and a second scale representation of the input image;
generating, by the computing system, a first scale representation of the generated image and a second scale representation of the generated image;
generating, by the computing system, a first combined image based on the first scale representation of the input image, the first scale representation of the generated image, and a first value;
generating, by the computing system, a second combined image based on the second scale representation of the input image, the second scale representation of the generated image, and a second value different from the first value; and
blending, by the computing system, the first combined image with the second combined image to generate an output image.