Machine learning diffusion model with image encoder trained for synthetic image generation
US12346995B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 21, 2023 |
| Grant date | Jul 1, 2025 |
| Priority date | — |
| Expiry date | Aug 26, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/171
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides systems and methods for generating a synthesized image of a user with a trained machine learning diffusion model. In one example, a computing system includes one or more processors configured to execute instructions stored in memory to execute a trained machine learning diffusion model including an image encoder, a text encoder, and a diffusion model. The image encoder is configured to receive an image of a user and generate a set of embeddings that semantically describe visual features of the user based at least on the image of the user. The text encoder is configured to receive the set of embeddings and generate an input feature vector based at least on the set of embeddings. The diffusion model is configured to receive the input feature vector and generate a synthesized image of the user based at least on the input feature vector.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.