DIFFUSION MODEL MULTI-PERSON IMAGE GENERATION

    公开(公告)号:US20250166264A1

    公开(公告)日:2025-05-22

    申请号:US18947989

    申请日:2024-11-14

    Applicant: Snap Inc.

    Abstract: Methods and systems are disclosed for generating personalized images using one or more diffusion models. The methods and systems access first and second artificial personalized images generated by first and second generative machine learning models, the first generative machine learning model trained to generate the first artificial personalized image comprising a depiction of a first person, the second generative machine learning model trained to generate the second artificial personalized image comprising a depiction of a second person. The methods and systems generate a foreground image that combines the depiction of the first person in the first artificial personalized image with the depiction of the second person in the second artificial personalized image. The methods and systems access generate a new artificial image comprising the foreground image on a background having visual attributes that correspond to the background information.

    Generating virtual hairstyle using latent space projectors

    公开(公告)号:US12277639B2

    公开(公告)日:2025-04-15

    申请号:US18149007

    申请日:2022-12-30

    Applicant: Snap Inc.

    Abstract: Embodiments enable virtual hair generation. The virtual hair generation can be performed by generating a first image of a face using a GAN model, applying 3D virtual hair on the first image to generate a second image with 3D virtual hair, projecting the second image with 3D virtual hair into a GAN latent space to generate a third image with virtual hair, performing a blend of the virtual hair with the first image of the face to generate a new image with new virtual hair that corresponds to the 3D virtual hair, training a neural network that receives the second image with the 3D virtual hair and provides an output image with virtual hair, and generating using the trained neural network, a particular output image with hair based on a particular input image with 3D virtual hair.

    GENERATING VIRTUAL HAIRSTYLE USING LATENT SPACE PROJECTORS

    公开(公告)号:US20240221259A1

    公开(公告)日:2024-07-04

    申请号:US18149007

    申请日:2022-12-30

    Applicant: Snap Inc.

    CPC classification number: G06T13/40 G06N3/094 G06T19/006

    Abstract: The subject technology generates a first image of a face using a GAN model. The subject technology applies 3D virtual hair on the first image to generate a second image with 3D virtual hair. The subject technology projects the second image with 3D virtual hair into a GAN latent space to generate a third image with realistic virtual hair. The subject technology performs a blend of the realistic virtual hair with the first image of the face to generate a new image with new realistic hair that corresponds to the 3D virtual hair. The subject technology trains a neural network that receives the second image with the 3D virtual hair and provides an output image with realistic virtual hair. The subject technology generates using the trained neural network, a particular output image with realistic hair based on a particular input image with 3D virtual hair.

    PHOTO-REALISTIC TEMPORALLY STABLE HAIRSTYLE CHANGE IN REAL-TIME

    公开(公告)号:US20250022264A1

    公开(公告)日:2025-01-16

    申请号:US18221241

    申请日:2023-07-12

    Applicant: Snap Inc.

    Abstract: The subject technology trains a neural network based on a training process. The subject technology selects a frame from an input video, the selected frame comprising image data including a representation of a face and hair, the representation of the hair being masked. The subject technology determines a previous predicted frame. The subject technology concatenates the selected frame and the previous predicted frame to generate a concatenated frame, the concatenated frame being provided to the neural network. The subject technology generates, using the neural network, a set of outputs including an output tensor, warping field, and a soft mask. The subject technology performs, using a warping field, a warp of the selected frame and the output tensor. The subject technology generates a prediction corresponding to a corrected texture rendering of the selected frame.

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