Utilizing a generative neural network to interactively create and modify digital images based on natural language feedback
US12148119B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 14, 2022 |
| Grant date | Nov 19, 2024 |
| Priority date | — |
| Expiry date | May 21, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/223
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.