Adversarial learning of photorealistic post-processing of simulation with privileged information
US10643320B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 12, 2018 |
| Grant date | May 5, 2020 |
| Priority date | — |
| Expiry date | Aug 2, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30252
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and method for generating photorealistic images include training a generative adversarial network (GAN) model by jointly learning a first generator, a first discriminator, and a set of predictors through an iterative process of optimizing a minimax objective. The first discriminator learns to determine a synthetic-to-real image from a real image. The first generator learns to generate the synthetic-to-real image from a synthetic image such that the first discriminator determines the synthetic-to-real image is real. The set of predictors learn to predict at least one of a semantic segmentation labeled data and a privileged information from the synthetic-to-real image based on at least one of a known semantic segmentation labeled data and a known privileged information corresponding to the synthetic image. Once trained, the GAN model may generate one or more photorealistic images using the trained GAN model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.