Retargeting skeleton motion sequences through cycle consistency adversarial training of a motion synthesis neural network with a forward kinematics layer
US10546408B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 20, 2018 |
| Grant date | Jan 28, 2020 |
| Priority date | — |
| Expiry date | Mar 28, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2213/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to methods, non-transitory computer readable media, and systems that use a motion synthesis neural network with a forward kinematics layer to generate a motion sequence for a target skeleton based on an initial motion sequence for an initial skeleton. In certain embodiments, the methods, non-transitory computer readable media, and systems use a motion synthesis neural network comprising an encoder recurrent neural network, a decoder recurrent neural network, and a forward kinematics layer to retarget motion sequences. To train the motion synthesis neural network to retarget such motion sequences, in some implementations, the disclosed methods, non-transitory computer readable media, and systems modify parameters of the motion synthesis neural network based on one or both of an adversarial loss and a cycle consistency loss.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.