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公开(公告)号:US20210192198A1
公开(公告)日:2021-06-24
申请号:US17138177
申请日:2020-12-30
Applicant: Snap Inc.
Inventor: Sergey Tulyakov , Roman Furko , Aleksei Stoliar
Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.
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公开(公告)号:US12094135B2
公开(公告)日:2024-09-17
申请号:US17829644
申请日:2022-06-01
Applicant: Snap Inc.
Inventor: Sergey Tulyakov , Roman Furko , Aleksei Stoliar
IPC: G06T7/277 , G06F18/214 , G06T7/246 , G06T7/73 , G06T11/60 , G06V10/25 , G06V10/62 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/82 , G06V20/20 , G06V20/40 , G06V40/10 , G06V40/16
CPC classification number: G06T7/277 , G06F18/2148 , G06F18/2155 , G06T7/246 , G06T7/73 , G06T11/60 , G06V10/25 , G06V10/764 , G06V10/7747 , G06V10/7753 , G06V10/776 , G06V10/82 , G06V20/20 , G06V20/40 , G06V40/10 , G06V40/168 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132 , G06V10/62 , G06V2201/07
Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.
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公开(公告)号:US10909357B1
公开(公告)日:2021-02-02
申请号:US16277710
申请日:2019-02-15
Applicant: Snap Inc.
Inventor: Sergey Tulyakov , Roman Furko , Aleksei Stoliar
Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.
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公开(公告)号:US20240185512A1
公开(公告)日:2024-06-06
申请号:US18061752
申请日:2022-12-05
Applicant: Snap Inc.
Inventor: Roman Furko , Vladyslav Horbatiuk , Amir Iagudin , James Supancic, III
CPC classification number: G06T15/205 , G06T7/246 , G06T19/006 , G06V20/20 , G06V20/46 , G06V40/107 , G06T2207/10016 , G06T2207/30196 , G06T2215/16
Abstract: A wrist tracking process is provided for use in Augmented Reality (AR) applications. A computing system captures video frame tracking data of a wrist of a user and generates 3D parameter data of the user's wrist based on the video frame tracking data. The computing system generates 3D render data of a virtual item based on the 3D parameter data of the user's wrist, and 3D model data of a physical item represented by the virtual item. The computing system generates video frame AR data based on the 3D render data and the video frame tracking data. The computing system provides an AR user interface to the user based on the video frame AR data.
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公开(公告)号:US20220292866A1
公开(公告)日:2022-09-15
申请号:US17829644
申请日:2022-06-01
Applicant: Snap Inc.
Inventor: Sergey Tulyakov , Roman Furko , Aleksei Stoliar
Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.
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公开(公告)号:US11354922B2
公开(公告)日:2022-06-07
申请号:US17138177
申请日:2020-12-30
Applicant: Snap Inc.
Inventor: Sergey Tulyakov , Roman Furko , Aleksei Stoliar
Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.
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