公开(公告)号：US20240096018A1

公开(公告)日：2024-03-21

申请号：US17932640

申请日：2022-09-15

Applicant: Lemon Inc.

Inventor： Tiancheng Zhi ,  Shen Sang ,  Guoxian Song ,  Chunpong Lai ,  Jing Liu ,  Linjie Luo

IPC: G06T17/20

CPC classification number: G06T17/20 ,  G06T2210/62

Abstract: Systems and methods for rendering a translucent object are provided. In one aspect, the system includes a processor coupled to a storage medium that stores instructions, which, upon execution by the processor, cause the processor to receive at least one mesh representing at least one translucent object. For each pixel to be rendered, the processor performs a rasterization-based differentiable rendering of the pixel to be rendered using the at least one mesh and determines a plurality of values for the pixel to be rendered based on the rasterization-based differentiable rendering. The rasterization-based differentiable rendering can include performing a probabilistic rasterization process along with aggregation techniques to compute the plurality of values for the pixel to be rendered. The plurality of values includes a set of color channel values and an opacity channel value. Once values are determined for all pixels, an image can be rendered.

公开(公告)号：US11928183B2

公开(公告)日：2024-03-12

申请号：US17532537

申请日：2021-11-22

Applicant: LEMON INC.

Inventor： Jingna Sun ,  Weihong Zeng ,  Peibin Chen ,  Xu Wang ,  Chunpong Lai ,  Shen Sang ,  Jing Liu

IPC: G06F18/214 ,  G06F16/532 ,  G06F18/24 ,  G06V40/16

CPC classification number: G06F18/2155 ,  G06F16/532 ,  G06F18/24 ,  G06V40/168

Abstract: An image processing method includes acquiring a set of image samples for training an attribute recognition model, wherein the set of image samples includes a first subset of image samples with category labels and a second subset of image samples without category labels; training a sample prediction model using the first subset of image samples, and predicting categories of the image samples in the second subset of image samples using the trained sample prediction model; determining a category distribution of the set of image samples based on the category labels of the first subset of image samples and the predicted categories of the second subset of image samples; and acquiring a new image sample if the determined category distribution does not conform to the expected category distribution, and adding the acquired new image sample to the set of image samples.

公开(公告)号：US20230146676A1

公开(公告)日：2023-05-11

申请号：US17519711

申请日：2021-11-05

Applicant: Lemon Inc.

Inventor： Jing Liu ,  Chunpong Lai ,  Guoxian Song ,  Linjie Luo

IPC: G06T9/00 ,  G06T11/60 ,  G06N3/08

CPC classification number: G06T9/002 ,  G06T11/60 ,  G06N3/08

Abstract: Systems and methods directed to controlling the similarity between stylized portraits and an original photo are described. In examples, an input image is received and encoded using a variational autoencoder to generate a latent vector. The latent vector may be blended with latent vectors that best represent a face in the original user portrait image. The resulting blended latent vector may be provided to a generative adversarial network (GAN) generator to generate a controlled stylized image. In examples, one or more layers of the stylized GAN generator may be swapped with one or more layers of the original GAN generator. Accordingly, a user can interactively determine how much stylization vs. personalization should be included in a resulting stylized portrait.