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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