Patents by Inventor Cameron Y. Smith

Cameron Y. Smith has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Face anonymization in digital images
    Patent number: 12688325
    Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.
    Type: Grant
    Filed: July 21, 2023
    Date of Patent: July 21, 2026
    Assignee: Adobe Inc.
    Inventors: Yang Yang, Zhixin Shu, Shabnam Ghadar, Jingwan Lu, Jakub Fiser, Elya Schechtman, Cameron Y. Smith, Baldo Antonio Faieta, Alex Charles Filipkowski
  • Face Anonymization in Digital Images
    Publication number: 20230360299
    Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.
    Type: Application
    Filed: July 21, 2023
    Publication date: November 9, 2023
    Applicant: Adobe Inc.
    Inventors: Yang Yang, Zhixin Shu, Shabnam Ghadar, Jingwan Lu, Jakub Fiser, Elya Schechtman, Cameron Y. Smith, Baldo Antonio Faieta, Alex Charles Filipkowski
  • Face anonymization in digital images
    Patent number: 11748928
    Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.
    Type: Grant
    Filed: November 10, 2020
    Date of Patent: September 5, 2023
    Assignee: Adobe Inc.
    Inventors: Yang Yang, Zhixin Shu, Shabnam Ghadar, Jingwan Lu, Jakub Fiser, Elya Schechtman, Cameron Y. Smith, Baldo Antonio Faieta, Alex Charles Filipkowski
  • Face Anonymization in Digital Images
    Publication number: 20220148243
    Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.
    Type: Application
    Filed: November 10, 2020
    Publication date: May 12, 2022
    Applicant: Adobe Inc.
    Inventors: Yang Yang, Zhixin Shu, Shabnam Ghadar, Jingwan Lu, Jakub Fiser, Elya Schechtman, Cameron Y. Smith, Baldo Antonio Faieta, Alex Charles Filipkowski