Transferring Style in Motion Capture Sequences with Adversarial Learning

Abstract : We focus on style transfer for sequential data in a supervised setting. Assuming sequential data include both content and style information we want to learn models able to transform a sequence into another one with the same content information but with the style of another one, from a training dataset where content and style labels are available. Following works on image generation and edition with adversarial learning we explore the design of neural network architectures for the task of sequence edition that we apply to motion capture sequences.
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02100672
Contributor : Thierry Artieres <>
Submitted on : Tuesday, April 16, 2019 - 10:33:13 AM
Last modification on : Friday, July 5, 2019 - 3:26:03 PM

File

es2018-188.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02100672, version 1

Citation

Qi Wang, Mickaël Chen, Thierry Artières, Ludovic Denoyer. Transferring Style in Motion Capture Sequences with Adversarial Learning. ESANN, Apr 2018, Bruges, Belgium. ⟨hal-02100672⟩

Share

Metrics

Record views

47

Files downloads

38