US 11,900,519 B2
Disentangling latent representations for image reenactment
Kevin Duarte, Orlando, FL (US); Wei-An Lin, San Jose, CA (US); Ratheesh Kalarot, San Jose, CA (US); Shabnam Ghadar, Menlo Park, CA (US); Jingwan Lu, Sunnyvale, CA (US); Elya Shechtman, Seattle, WA (US); and John Thomas Nack, San Jose, CA (US)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Nov. 17, 2021, as Appl. No. 17/455,318.
Prior Publication US 2023/0154088 A1, May 18, 2023
Int. Cl. G06T 13/40 (2011.01); G06T 5/50 (2006.01); G06N 3/045 (2023.01)
CPC G06T 13/40 (2013.01) [G06N 3/045 (2023.01); G06T 5/50 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for image processing, comprising:
encoding features of a source image to obtain a source appearance encoding that represents inherent attributes of a face in the source image;
encoding features of a target image to obtain a target non-appearance encoding that represents contextual attributes of the target image;
combining the source appearance encoding and the target non-appearance encoding using a reconstruction network to obtain combined image features, wherein the combined image features comprise a reconstructed vector in a latent space of a generator network; and
generating, using the generator network, a modified target image based on the combined image features, wherein the modified target image includes the inherent attributes of the face in the source image together with the contextual attributes of the target image.