Invention Grant
- Patent Title: Utilizing a generative neural network to interactively create and modify digital images based on natural language feedback
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Application No.: US17576091Application Date: 2022-01-14
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Publication No.: US12148119B2Publication Date: 2024-11-19
- Inventor: Ruiyi Zhang , Yufan Zhou , Christopher Tensmeyer , Jiuxiang Gu , Tong Yu , Tong Sun
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06T5/00
- IPC: G06T5/00 ; G06N3/04 ; G06T3/10 ; G06T5/60 ; G06T11/00 ; G06T11/80 ; G10L15/22 ; G10L15/26

Abstract:
The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a neural network framework for interactive multi-round image generation from natural language inputs. Specifically, the disclosed systems provide an intelligent framework (i.e., a text-based interactive image generation model) that facilitates a multi-round image generation and editing workflow that comports with arbitrary input text and synchronous interaction. In particular embodiments, the disclosed systems utilize natural language feedback for conditioning a generative neural network that performs text-to-image generation and text-guided image modification. For example, the disclosed systems utilize a trained model to inject textual features from natural language feedback into a unified joint embedding space for generating text-informed style vectors. In turn, the disclosed systems can generate an image with semantically meaningful features that map to the natural language feedback. Moreover, the disclosed systems can persist these semantically meaningful features throughout a refinement process and across generated images.
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