公开(公告)号：US12112573B2

公开(公告)日：2024-10-08

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 ,  G06F18/21 ,  G06T7/246 ,  G06T13/40 ,  G06T13/80 ,  G06V10/24 ,  G06V40/16

CPC classification number: G06V40/176 ,  G06F18/2193 ,  G06T7/251 ,  G06T13/40 ,  G06T13/80 ,  G06V10/242 ,  G06V40/171 ,  G06T2207/20084 ,  G06T2207/30201

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.

公开(公告)号：US20230377368A1

公开(公告)日：2023-11-23

申请号：US17751393

申请日：2022-05-23

Applicant: LEMON Inc.

Inventor： Shuo CHENG ,  Guoxian SONG ,  Wanchun MA ,  Chao Wang ,  Linjie LUO

IPC: G06V40/16 ,  G06V10/74 ,  G06V10/82 ,  G06T11/60

CPC classification number: G06V40/172 ,  G06V10/761 ,  G06V10/82 ,  G06T11/60

Abstract: Methods and systems for generating synthetic images based on an input image are described. The method may include receiving an input image; generating, using an encoder, a first latent code vector representation based on the input image; receiving a latent code corresponding to a feature to be added to the input image; modifying the first latent code vector representation based on the latent code corresponding to the feature to be added; generating, by an image decoder, a synthesized image based on the modified first latent code vector representation; identifying, using a landmark detector, one or more landmarks in the base image; identifying, using a landmark detector, one or more landmarks in the synthesized image; determining a measure of similarity between the landmark identified on the base image and the landmark identified in the synthesized image; and discarding the synthesized image based on the comparison.

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.