Generating human motion sequences utilizing unsupervised learning of discretized features via a neural network encoder-decoder
US12067661B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 16, 2022 |
| Grant date | Aug 20, 2024 |
| Priority date | — |
| Expiry date | Dec 19, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T17/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing unsupervised learning of discrete human motions to generate digital human motion sequences. The disclosed system utilizes an encoder of a discretized motion model to extract a sequence of latent feature representations from a human motion sequence in an unlabeled digital scene. The disclosed system also determines sampling probabilities from the sequence of latent feature representations in connection with a codebook of discretized feature representations associated with human motions. The disclosed system converts the sequence of latent feature representations into a sequence of discretized feature representations by sampling from the codebook based on the sampling probabilities. Additionally, the disclosed system utilizes a decoder to reconstruct a human motion sequence from the sequence of discretized feature representations. The disclosed system also utilizes a reconstruction loss and a distribution loss to learn parameters of the discretized motion model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.