公开(公告)号：US20200051306A1

公开(公告)日：2020-02-13

申请号：US16655686

申请日：2019-10-17

Applicant: INTEL CORPORATION

Inventor： Minje Park ,  Tae-Hoon Kim ,  Myung-Ho Ju ,  Jihyeon Yi ,  Xiaolu Shen ,  Lidan Zhang ,  Qiang Li

IPC: G06T13/40 ,  G06T7/73

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor. The machine learning component may be trained using a set of training images that depict human facial expressions and avatar animation authored by professional animators to reflect facial expressions depicted in the set of training images.

公开(公告)号：US10475225B2

公开(公告)日：2019-11-12

申请号：US15124811

申请日：2015-12-18

Applicant: INTEL CORPORATION

Inventor： Minje Park ,  Tae-Hoon Kim ,  Myung-Ho Ju ,  Jihyeon Yi ,  Xiaolu Shen ,  Lidan Zhang ,  Qiang Li

IPC: G06T13/40 ,  G06T7/73 ,  G06T17/20

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor. The machine learning component may be trained using a set of training images that depict human facial expressions and avatar animation authored by professional animators to reflect facial expressions depicted in the set of training images.