公开(公告)号：US12051168B2

公开(公告)日：2024-07-30

申请号：US17932645

申请日：2022-09-15

Applicant: Lemon Inc. ,  Beijing Zitiao Network Technology Co., Ltd.

Inventor： Hongyi Xu ,  Tao Hu ,  Linjie Luo

IPC: G06T19/20 ,  G06T7/73 ,  G06T15/04 ,  G06T17/20

CPC classification number: G06T19/20 ,  G06T7/75 ,  G06T15/04 ,  G06T17/20 ,  G06T2207/20084 ,  G06T2210/16 ,  G06T2219/2004

Abstract: Systems and methods are provided that include a processor executing an avatar generation program to obtain driving view(s), calculate a skeletal pose of the user, and generate a coarse human mesh based on a template mesh and the skeletal pose of the user. The program further constructs a texture map based on the driving view(s) and the coarse human mesh, extracts a plurality of image features from the texture map, the image features being aligned to a UV map, and constructs a UV positional map based on the coarse human mesh. The program further extracts a plurality of pose features from the UV positional map, the pose features being aligned to the UV map, generates a plurality of pose-image features based on the UV map-aligned image features and UV map-aligned pose features, and renders an avatar based on the plurality of pose-image features.

公开(公告)号：US20230410267A1

公开(公告)日：2023-12-21

申请号：US17807527

申请日：2022-06-17

Applicant: Lemon Inc. ,  Beijing Zitiao Network Technology Co., Ltd.

Inventor： Guoxian Song ,  Jing Liu ,  Weihong Zeng ,  Jingna Sun ,  Xu Wang ,  Linjie Luo

IPC: G06T5/50 ,  G06T3/40 ,  G06V40/16

CPC classification number: G06T5/50 ,  G06T3/4046 ,  G06V40/168 ,  G06T2207/20084 ,  G06T2207/20132 ,  G06T2207/20081 ,  G06T2207/20221 ,  G06T2207/30201

Abstract: Methods and systems for enlarging a stylized region of an image are disclosed that include receiving an input image, generating, using a first generative adversarial network (GAN) generator, a first stylized image, based on the input image, normalizing the input image, generating, using a second generative adversarial network (GAN) generator, a second stylized image, based on the normalized input image, blending the first stylized image and the second stylized image to obtain a third stylized image, and providing the third stylized image as an output.

公开(公告)号：US11803996B2

公开(公告)日：2023-10-31

申请号：US17390440

申请日：2021-07-30

Applicant: Lemon Inc.

Inventor： Wanchun Ma ,  Shuo Cheng ,  Chao Wang ,  Michael Leong Hou Tay ,  Linjie Luo

IPC: G06T13/40 ,  G06N3/08 ,  G06V40/16

CPC classification number: G06T13/40 ,  G06N3/08 ,  G06V40/162 ,  G06V40/171 ,  G06V40/176

Abstract: Techniques for face tracking comprise receiving landmark data associated with a plurality of images indicative of at least one facial part. Representative images corresponding to the plurality of images may be generated based on the landmark data. Each representative image may depict a plurality of segments, and each segment may correspond to a region of the at least one facial part. The plurality of images and corresponding representative images may be input into a neural network to train the neural network to predict a feature associated with a subsequently received image comprising a face. An animation associated with a facial expression may be controlled based on output from the trained neural network.

公开(公告)号：US20230046286A1

公开(公告)日：2023-02-16

申请号：US17402344

申请日：2021-08-13

Applicant: Lemon Inc.

Inventor： Michael Leong Hou Tay ,  Wanchun Ma ,  Shuo Cheng ,  Chao Wang ,  Linjie Luo

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/32 ,  G06T13/80 ,  G06T13/40 ,  G06T7/246

Abstract: The present disclosure describes techniques for facial expression recognition. A first loss function may be determined based on a first set of feature vectors associated with a first set of images depicting facial expressions and a first set of labels indicative of the facial expressions. A second loss function may be determined based on a second set of feature vectors associated with a second set of images depicting asymmetric facial expressions and a second set of labels indicative of the asymmetric facial expressions. The first loss function and the second loss function may be used to determine a maximum loss function. The maximum loss function may be applied during training of a model. The trained model may be configured to predict at least one asymmetric facial expression in a subsequently received image.