US 12,067,661 B2
Generating human motion sequences utilizing unsupervised learning of discretized features via a neural network encoder-decoder
Jun Saito, Seattle, WA (US); Nitin Saini, Tübingen (DE); and Ruben Villegas, Ann Arbor, MI (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Feb. 16, 2022, as Appl. No. 17/651,330.
Prior Publication US 2023/0260182 A1, Aug. 17, 2023
Int. Cl. G06T 13/40 (2011.01); G06T 9/00 (2006.01); G06T 13/20 (2011.01); G06T 17/00 (2006.01)
CPC G06T 13/40 (2013.01) [G06T 9/001 (2013.01); G06T 13/205 (2013.01); G06T 17/00 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
generating, utilizing an encoder of a discretized motion model, a sequence of latent feature representations of a human motion sequence from an unlabeled digital scene;
converting, utilizing a codebook of the discretized motion model, the sequence of latent feature representations into a sequence of discretized feature representations by mapping the sequence of latent feature representations to a plurality of learned latent feature representations corresponding to human motions; and
generating, utilizing a decoder of the discretized motion model, digital content comprising a reconstructed human motion sequence based on the sequence of discretized feature representations.