US 12,307,616 B2
Techniques for re-aging faces in images and video frames
Gaspard Zoss, Zurich (CH); Derek Edward Bradley, Zurich (CH); Prashanth Chandran, Zurich (CH); Paulo Fabiano Urnau Gotardo, Zurich (CH); and Eftychios Sifakis, Verona, WI (US)
Assigned to Disney Enterprises, INC., Burbank, CA (US); and ETH Zürich (Eidgenössische Technische Hochschule Zürich), Zürich (CH)
Filed by DISNEY ENTERPRISES, INC., Burbank, CA (US); and ETH Zürich (Eidgenössische Technische Hochschule Zürich), Zürich (CH)
Filed on Sep. 13, 2021, as Appl. No. 17/473,232.
Prior Publication US 2023/0080639 A1, Mar. 16, 2023
Int. Cl. G06T 19/20 (2011.01); G06N 3/08 (2023.01); G06T 7/149 (2017.01); G06T 17/20 (2006.01)
CPC G06T 19/20 (2013.01) [G06N 3/08 (2013.01); G06T 7/149 (2017.01); G06T 17/20 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30201 (2013.01); G06T 2219/2021 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A computer-implemented method for re-aging a face included in a first image, the method comprising:
generating an input age image corresponding to an input age associated with the face in the first image and a target age image associated with a target age of the face in the first image, wherein the input age image includes a first plurality of pixels having age values indicating the input age and the target age image includes a second plurality of pixels having age values indicating the target age, and wherein the first image, the input age image, and the target age image have the same spatial resolution;
generating, via a machine learning model, a second image based on a multi-channel tensor provided to the machine learning model at execution, wherein the multi-channel tensor comprises (i) the first image that includes the face, (ii) an input age image, and (iii) a target age image, wherein the second image includes one or more differences from the first image; and
combining the first image and the second image into a third image.