公开(公告)号：US20230162320A1

公开(公告)日：2023-05-25

申请号：US17534631

申请日：2021-11-24

Applicant: Lemon Inc.

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

IPC: G06T3/00 ,  G06N3/04 ,  G06T5/50

CPC classification number: G06T3/00 ,  G06N3/0454 ,  G06T5/50

Abstract: Methods and systems for generating a texturized image are disclosed. Some examples may include: receiving an input image, receiving an exemplar texture image, generating, using an encoder, a first latent code vector representation based on the input image, generating, using a generative adversarial network generator, a second latent code vector representation based on the exemplar texture image, blending the first latent code vector representation and the second latent code vector representation to obtain a blended latent code vector representation, generating, by the GAN generator, a texturized image based on the blended latent code vector representation and providing the texturized image as an output image.

公开(公告)号：US20250126307A1

公开(公告)日：2025-04-17

申请号：US18913935

申请日：2024-10-11

Applicant: Douyin Vision Co., Ltd. ,  Lemon Inc.

Inventor： Jing Liu ,  Bo Wang ,  Shu Shi

IPC: H04N21/234 ,  H04N21/238

Abstract: Embodiment of the disclosure disclose a method, apparatus, device, storage medium and product for video bitrate adjustment, and the method includes: analyzing and determining a current picture quality evaluation result of a target video stream in a current bitrate regulation period based on a video frame; and determining a target bitrate based on a predetermined target picture quality evaluation standard associated with a bitrate and/or a picture quality change trend of the current picture quality evaluation result relative to a historical picture quality evaluation result in a historical bitrate regulation period.

公开(公告)号：US12148095B2

公开(公告)日：2024-11-19

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 ,  G06T17/10

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：US12106545B2

公开(公告)日：2024-10-01

申请号：US17534681

申请日：2021-11-24

Applicant: LEMON INC.

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

IPC: G06V10/764 ,  G06N3/08 ,  G06V10/72 ,  G06V10/82

CPC classification number: G06V10/764 ,  G06N3/08 ,  G06V10/72 ,  G06V10/82

Abstract: The present disclosure provides a training method and device for an image identifying model, and an image identifying method. The training method comprises: obtaining image samples of a plurality of categories; inputting image samples of each category into a feature extraction layer of the image identifying model to extract a feature vector of each image sample; calculating a statistical characteristic information of an actual distribution function corresponding to each category according to the feature vector of each image sample of the each category; establishing an augmented distribution function corresponding to the each category according to the statistical characteristic information; obtaining augmented sample features of the each category based on the augmented distribution function; and inputting feature vectors of the image samples and the augmented sample features into a classification layer of the image identifying model for supervised learning.