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公开(公告)号：US12272015B2
公开(公告)日：2025-04-08
申请号：US18653609
申请日：2024-05-02
Applicant: Snap Inc.
Inventor： Artem Bondich , Menglei Chai , Oleksandr Pyshchenko , Jian Ren , Sergey Tulyakov
Abstract: A messaging system performs neural network hair rendering for images provided by users of the messaging system. A method of neural network hair rendering includes processing a three-dimensional (3D) model of fake hair and a first real hair image depicting a first person to generate a fake hair structure, and encoding, using a fake hair encoder neural subnetwork, the fake hair structure to generate a coded fake hair structure. The method further includes processing, using a cross-domain structure embedding neural subnetwork, the coded fake hair structure to generate a fake and real hair structure, and encoding, using an appearance encoder neural subnetwork, a second real hair image depicting a second person having a second head to generate an appearance map. The method further includes processing, using a real appearance renderer neural subnetwork, the appearance map and the fake and real hair structure to generate a synthesized real image.
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公开(公告)号：US20250054201A1
公开(公告)日：2025-02-13
申请号：US18231886
申请日：2023-08-09
Applicant: Snap Inc.
Inventor： Ekaterina Deyneka , Andrey Alejandrovich Gomez Zharkov , Sergey Tulyakov , Aleksei Stoliar , Konstantin Gudkov
IPC: G06T11/00 , G06V10/774 , G06V10/776
Abstract: Methods and systems are disclosed for enhancing or modifying an image by a machine learning model. The methods and systems receive an image depicting a real-world object. The methods and systems analyze the image using a machine learning model to generate a modified image that depicts one or more augmented reality stylizations overlaid on the real-world object, the machine learning model trained in multiple stages having different training data sets and different conditions applied in each of the multiple stages. The methods and systems present the modified image on a device.
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公开(公告)号：US12223402B2
公开(公告)日：2025-02-11
申请号：US17741614
申请日：2022-05-11
Applicant: Snap Inc.
Inventor： Eric Buehl , Jordan Hurwitz , Sergey Tulyakov , Shubham Vij
Abstract: Disclosed are various embodiments for implementing computational tasks in a cloud environment in one or more operating system level virtualized containers. A parameter file can specify different parameters including hardware parameters, library parameters, user code parameters, and job parameters (e.g., sets of hyperparameters). The parameter file can be converted via a mapping and implemented in a cloud-based container platform.
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公开(公告)号：US20240273809A1
公开(公告)日：2024-08-15
申请号：US18644653
申请日：2024-04-24
Applicant: Snap Inc.
Inventor： Zeng Huang , Jian Ren , Sergey Tulyakov , Menglei Chai , Kyle Olszewski , Huan Wang
CPC classification number: G06T15/06 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.
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公开(公告)号：US12002146B2
公开(公告)日：2024-06-04
申请号：US17656778
申请日：2022-03-28
Applicant: Snap Inc.
Inventor： Zeng Huang , Jian Ren , Sergey Tulyakov , Menglei Chai , Kyle Olszewski , Huan Wang
CPC classification number: G06T15/06 , G06T7/97 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.
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Patent Agency Ranking
公开(公告)号：US20230386158A1
公开(公告)日：2023-11-30
申请号：US17814391
申请日：2022-07-22
Applicant: Snap Inc.
Inventor： Menglei Chai , Sergey Tulyakov , Jian Ren , Hsin-Ying Lee , Kyle Olszewski , Zeng Huang , Zezhou Cheng
CPC classification number: G06T19/20 , G06T17/00 , G06T2219/2012 , G06T2219/2021
Abstract: Systems, computer readable media, and methods herein describe an editing system where a three-dimensional (3D) object can be edited by editing a 2D sketch or 2D RGB views of the 3D object. The editing system uses multi-modal (MM) variational auto-decoders (VADs)(MM-VADs) that are trained with a shared latent space that enables editing 3D objects by editing 2D sketches of the 3D objects. The system determines a latent code that corresponds to an edited or sketched 2D sketch. The latent code is then used to generate a 3D object using the MM-VADs with the latent code as input. The latent space is divided into a latent space for shapes and a latent space for colors. The MM-VADs are trained with variational auto-encoders (VAE) and a ground truth.
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公开(公告)号：US20230334327A1
公开(公告)日：2023-10-19
申请号：US18213145
申请日：2023-06-22
Applicant: Snap Inc.
Inventor： Sergey Tulyakov , Sergei Korolev , Aleksei Stoliar , Maksim Gusarov , Sergei Kotcur , Christopher Yale Crutchfield , Andrew Wan
IPC: G06N3/088 , G06N3/08 , G06F18/21 , G06F18/214 , G06N3/045 , G06V10/764 , G06V10/774 , G06V10/778 , G06V10/82
CPC classification number: G06N3/088 , G06N3/08 , G06F18/2185 , G06F18/2148 , G06N3/045 , G06V10/764 , G06V10/7747 , G06V10/7788 , G06V10/82
Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.
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公开(公告)号：US11727280B2
公开(公告)日：2023-08-15
申请号：US17189563
申请日：2021-03-02
Applicant: Snap Inc.
Inventor： Sergey Tulyakov , Sergei Korolev , Aleksei Stoliar , Maksim Gusarov , Sergei Kotcur , Christopher Yale Crutchfield , Andrew Wan
IPC: G06N3/088 , G06N3/08 , G06F18/21 , G06F18/214 , G06N3/045 , G06V10/764 , G06V10/774 , G06V10/778 , G06V10/82
CPC classification number: G06N3/088 , G06F18/2148 , G06F18/2185 , G06N3/045 , G06N3/08 , G06V10/764 , G06V10/7747 , G06V10/7788 , G06V10/82
Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.
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公开(公告)号：US20220405637A1
公开(公告)日：2022-12-22
申请号：US17741614
申请日：2022-05-11
Applicant: Snap Inc.
Inventor： Eric Buehl , Jordan Hurwitz , Sergey Tulyakov , Shubham Vij
Abstract: Disclosed are various embodiments for implementing computational tasks in a cloud environment in one or more operating system level virtualized containers. A parameter file can specify different parameters including hardware parameters, library parameters, user code parameters, and job parameters (e.g., sets of hyperparameters). The parameter file can be converted via a mapping and implemented in a cloud-based container platform.
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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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