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    MULTI-DIMENSIONAL IMAGE STYLIZATION USING TRANSFER LEARNING
    1.
    发明公开
    MULTI-DIMENSIONAL IMAGE STYLIZATION USING TRANSFER LEARNING 审中-公开

    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

    IPC: G06V10/77 ,  G06V10/28 ,  G06V10/44

    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.

    Methods for a rasterization-based differentiable renderer for translucent objects
    3.
    发明授权
    Methods for a rasterization-based differentiable renderer for translucent objects 有权

    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.

    METHODS FOR A RASTERIZATION-BASED DIFFERENTIABLE RENDERER FOR TRANSLUCENT OBJECTS
    6.
    发明公开
    METHODS FOR A RASTERIZATION-BASED DIFFERENTIABLE RENDERER FOR TRANSLUCENT OBJECTS 审中-公开

    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.

    PORTRAIT STYLIZATION FRAMEWORK TO CONTROL THE SIMILARITY BETWEEN STYLIZED PORTRAITS AND ORIGINAL PHOTO
    7.
    发明公开
    PORTRAIT STYLIZATION FRAMEWORK TO CONTROL THE SIMILARITY BETWEEN STYLIZED PORTRAITS AND ORIGINAL PHOTO 审中-公开

    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.

    Cascaded domain bridging for image generation
    8.
    发明授权
    Cascaded domain bridging for image generation 有权

    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

    IPC: G06T15/00 ,  G06T7/11 ,  G06T15/02

    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.

    Agilegan-based stylization method to enlarge a style region
    9.
    发明授权
    Agilegan-based stylization method to enlarge a style region 有权

    公开(公告)号：US12190481B2

    公开(公告)日：2025-01-07

    申请号：US17807527

    申请日：2022-06-17

    Applicant: Lemon Inc.

    Inventor： Guoxian Song ,  Jing Liu ,  Weihong Zeng ,  Jingna Sun ,  Xu Wang ,  Linjie Luo

    IPC: G06T5/50 ,  G06T3/4046 ,  G06V40/16

    Abstract: Methods and systems for enlarging a stylized region of an image are disclosed that include receiving an input image, generating, using a first generative adversarial network (GAN) generator, a first stylized image, based on the input image, normalizing the input image, generating, using a second generative adversarial network (GAN) generator, a second stylized image, based on the normalized input image, blending the first stylized image and the second stylized image to obtain a third stylized image, and providing the third stylized image as an output.

    Agilegan-based refinement method and framework for consistent texture generation
    10.
    发明授权
    Agilegan-based refinement method and framework for consistent texture generation 有权

    公开(公告)号：US12169907B2

    公开(公告)日：2024-12-17

    申请号：US17534631

    申请日：2021-11-24

    Applicant: Lemon Inc.

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

    IPC: G06T5/50 ,  G06N3/045 ,  G06T3/00 ,  G06T3/04 ,  G06V10/774 ,  G06V10/82

    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.

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