Invention Publication
- Patent Title: GENERATING AND MODIFYING DIGITAL IMAGES USING A JOINT FEATURE STYLE LATENT SPACE OF A GENERATIVE NEURAL NETWORK
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Application No.: US17655739Application Date: 2022-03-21
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Publication No.: US20230316606A1Publication Date: 2023-10-05
- Inventor: Hui Qu , Baldo Faieta , Cameron Smith , Elya Shechtman , Jingwan Lu , Ratheesh Kalarot , Richard Zhang , Saeid Motiian , Shabnam Ghadar , Wei-An Lin
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06T11/60

Abstract:
The present disclosure relates to systems, non-transitory computer-readable media, and methods for latent-based editing of digital images using a generative neural network. In particular, in one or more embodiments, the disclosed systems perform latent-based editing of a digital image by mapping a feature tensor and a set of style vectors for the digital image into a joint feature style space. In one or more implementations, the disclosed systems apply a joint feature style perturbation and/or modification vectors within the joint feature style space to determine modified style vectors and a modified feature tensor. Moreover, in one or more embodiments the disclosed systems generate a modified digital image utilizing a generative neural network from the modified style vectors and the modified feature tensor.
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