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：US20240265628A1

公开(公告)日：2024-08-08

申请号：US18165619

申请日：2023-02-07

Applicant: Lemon Inc.

Inventor： Hongyi XU ,  Sizhe AN ,  Yichun SHI ,  Guoxian SONG ,  Linjie LUO

IPC: G06T17/00 ,  G06T3/40 ,  G06T7/194 ,  G06T7/70 ,  G06T11/00 ,  G06T15/10 ,  G06T19/20

CPC classification number: G06T17/00 ,  G06T3/4053 ,  G06T7/194 ,  G06T7/70 ,  G06T11/00 ,  G06T15/10 ,  G06T19/20 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2210/12 ,  G06T2210/22 ,  G06T2219/2004 ,  G06T2219/2016

Abstract: A three-dimensional generative adversarial network includes a generator, a discriminator, and a renderer. The generator is configured to receive an intermediate latent code mapped from a latent code and a camera pose, generate two-dimensional backgrounds for a set of images, and generate, based on the intermediate latent code, multi-grid representation features. The renderer is configured to synthesize images based on the camera pose, a camera pose offset, and the multi-grid representation features; the camera pose offset being mapped from the latent code and the camera pose; and render a foreground mask. The discriminator is configured to supervise a training of the foreground mask with an up-sampled image and a super-resolved image.

公开(公告)号：US20240135627A1

公开(公告)日：2024-04-25

申请号：US18046077

申请日：2022-10-12

Applicant: Lemon INc.

Inventor： Guoxian SONG ,  Shen Sang ,  Tiancheng Zhi ,  Jing Liu ,  Linjie Luo

IPC: G06T15/02 ,  G06T7/11

CPC classification number: G06T15/02 ,  G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: A method of generating a style image is described. The method includes receiving an input image of a subject. The method further includes encoding the input image using a first encoder of a generative adversarial network (GAN) to obtain a first latent code. The method further includes decoding the first latent code using a first decoder of the GAN to obtain a normalized style image of the subject, wherein the GAN is trained using a loss function according to semantic regions of the input image and the normalized style image.

公开(公告)号：US20220375024A1

公开(公告)日：2022-11-24

申请号：US17321384

申请日：2021-05-14

Applicant: Lemon Inc.

Inventor： Linjie LUO ,  Guoxian SONG ,  Jing LIU ,  Wanchun MA

IPC: G06T3/00 ,  G06T11/00 ,  G06T5/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Systems and method directed to an inversion-consistent transfer learning framework for generating portrait stylization using only limited exemplars. In examples, an input image is received and encoded using a variational autoencoder to generate a latent vector. The latent vector may be provided to a generative adversarial network (GAN) generator to generate a stylized image. In examples, the variational autoencoder is trained using a plurality of images while keeping the weights of a pre-trained GAN generator fixed, where the pre-trained GAN generator acts as a decoder for the encoder. In other examples, a multi-path attribute aware generator is trained using a plurality of exemplar images and learning transfer using the pre-trained GAN generator.