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公开(公告)号：US20240273871A1
公开(公告)日：2024-08-15
申请号：US18168867
申请日：2023-02-14
Applicant: Lemon Inc.
Inventor： Guoxian Song , Hongyi Xu , Jing Liu , Tiancheng Zhi , Yichun Shi , Jianfeng Zhang , Zihang Jiang , Jiashi Feng , Shen Sang , Linjie Luo
CPC classification number: G06V10/7715 , G06V10/28 , G06V10/454
Abstract: A method for generating a multi-dimensional stylized image. The method includes providing input data into a latent space for a style conditioned multi-dimensional generator of a multi-dimensional generative model and generating the multi-dimensional stylized image from the input data by the style conditioned multi-dimensional generator. The method further includes synthesizing content for the multi-dimensional stylized image using a latent code and corresponding camera pose from the latent space to formulate an intermediate code to modulate synthesis convolution layers to generate feature images as multi-planar representations and synthesizing stylized feature images of the feature images for generating the multi-dimensional stylized image of the input data. The style conditioned multi-dimensional generator is tuned using a guided transfer learning process using a style prior generator.
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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
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.
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公开(公告)号：US12260485B2
公开(公告)日：2025-03-25
申请号：US18046077
申请日：2022-10-12
Applicant: Lemon Inc.
Inventor： Guoxian Song , Shen Sang , Tiancheng Zhi , Jing Liu , Linjie Luo
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.
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Patent Agency Ranking
公开(公告)号：US11978280B2
公开(公告)日：2024-05-07
申请号：US17529192
申请日：2021-11-17
Applicant: Lemon Inc.
Inventor： Jingna Sun , Peibin Chen , Weihong Zeng , Xu Wang , Jing Liu , Chunpong Lai , Shen Sang
IPC: G06K9/46 , G06F18/21 , G06F18/2113 , G06F18/22 , G06F18/2431 , G06V40/16
CPC classification number: G06V40/172 , G06F18/2113 , G06F18/217 , G06F18/22 , G06F18/2431
Abstract: A method is provided for evaluating an effect of classifying a fuzzy attribute of an object, the fuzzy attribute referring to an attribute, a boundary between two similar ones of a plurality of categories of which is blurred, wherein the method includes: generating a similarity-based ranked confusion matrix, which comprises: based on similarities of K categories of the fuzzy attribute of the object, ranking the K categories, where K is an integer greater than or equal to 2, generating a K×K all-zero initialization matrix, wherein an abscissa and an ordinate of the initialization matrix respectively represent predicted values and true values of the similarity-based ranked categories of the fuzzy attribute, and based on the true values and the predicted values of the category of the fuzzy attribute for the multiple object samples, updating values of corresponding elements in the initialization matrix; and displaying the similarity-based ranked confusion matrix.
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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.
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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.
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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
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.
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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.
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