公开(公告)号：US20250148816A1

公开(公告)日：2025-05-08

申请号：US18502868

申请日：2023-11-06

Applicant: Snap Inc.

Inventor： Maksim Gusarov ,  Kwot Sin Lee ,  Patrick Poirson ,  Chen Wang

IPC: G06V20/70 ,  G06F40/40 ,  G06N3/0455 ,  G06T11/00 ,  G06V10/774 ,  G06V20/20 ,  G06V20/40

Abstract: A second input image is generated by applying a target augmented reality (AR) effect to a first input image. The first input image and the second input image are provided to a first visual-semantic machine learning model to obtain output describing at least one feature of the target AR effect. The first visual-semantic machine learning model is fine-tuned from a second visual-semantic machine learning model by using training samples. Each training sample comprises a first training image, a second training image, and a training description of a given AR effect. The second training image is generated by applying the given AR effect to the first training image. A description of the target AR effect is selected based on the output of the visual-semantic machine learning model. The description of the target AR effect is stored in association with an identifier of the target AR effect.

公开(公告)号：US20250148218A1

公开(公告)日：2025-05-08

申请号：US18502679

申请日：2023-11-06

Applicant: Snap Inc.

Inventor： Maksim Gusarov ,  Kwot Sin Lee ,  Yanjia Li ,  Patrick Poirson ,  Chen Wang

IPC: G06F40/40 ,  G06T19/00 ,  G06V10/72

Abstract: A first image and a second image are accessed. The second image is generated by applying an augmented reality (AR) effect to the first image. The first image, the second image, and a prompt are provided to a visual-semantic machine learning model to obtain output describing at least one feature of the AR effect. A description of the AR effect is generated based on the output of the visual-semantic machine learning model. The description of the AR effect is stored in association with an identifier of the AR effect.

公开(公告)号：US12254049B2

公开(公告)日：2025-03-18

申请号：US18054420

申请日：2022-11-10

Applicant: Snap Inc.

Inventor： Kevin Sarabia Dela Rosa ,  Adel Elmalaha ,  Kwot Sin Lee ,  Patrick Poirson

IPC: G06F16/83 ,  G06F16/903 ,  G06F16/9035 ,  G06F16/907 ,  G06T19/00 ,  G06V10/74

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for performing operations comprising: receiving an image from a client device; applying a machine learning model to the image to generate an embedding query vector, the machine learning model being trained to encode a plurality of images and text into a common embedding space; searching, based on the embedding query vector, a database of augmented reality (AR) experiences to identify a subset of AR experiences associated with one or more embeddings that correspond to the embedding query vector; and transmitting to the client device the subset of AR experiences associated with the one or more embeddings that correspond to the embedding query vector.

公开(公告)号：US20240160673A1

公开(公告)日：2024-05-16

申请号：US18054420

申请日：2022-11-10

Applicant: Snap Inc.

Inventor： Kevin Sarabia Dela Rosa ,  Adel Elmalaha ,  Kwot Sin Lee ,  Patrick Poirson

IPC: G06F16/907 ,  G06F16/903 ,  G06F16/9035 ,  G06T19/00 ,  G06V10/74

CPC classification number: G06F16/907 ,  G06F16/90335 ,  G06F16/9035 ,  G06T19/006 ,  G06V10/761

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for performing operations comprising: receiving an image from a client device; applying a machine learning model to the image to generate an embedding query vector, the machine learning model being trained to encode a plurality of images and text into a common embedding space; searching, based on the embedding query vector, a database of augmented reality (AR) experiences to identify a subset of AR experiences associated with one or more embeddings that correspond to the embedding query vector; and transmitting to the client device the subset of AR experiences associated with the one or more embeddings that correspond to the embedding query vector.