169 citations · 185 across the 5 of their papers we have counts for
6 papers
Speech Modeling with a Hierarchical Transformer Dynamical VAE
Xiaoyu Lin, Xiaoyu Bie, Simon Leglaive +2
The dynamical variational autoencoders (DVAEs) are a family of latent-variable deep generative models that extends the VAE to model a sequence of observed data and a corresponding…
HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE
Xiaoyu Bie, Wen Guo, Simon Leglaive +3
Studies on the automatic processing of 3D human pose data have flourished in the recent past. In this paper, we are interested in the generation of plausible and diverse future hum…
A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
Xiaoyu Bie, Laurent Girin, Simon Leglaive +2
The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space lea…
Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders
Xiaoyu Bie, Simon Leglaive, Xavier Alameda-Pineda +1
Dynamical variational autoencoders (DVAEs) are a class of deep generative models with latent variables, dedicated to model time series of high-dimensional data. DVAEs can be consid…
Multi-Person Extreme Motion Prediction
Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda +1
Human motion prediction aims to forecast future poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackl…
Dynamical Variational Autoencoders: A Comprehensive Review
Laurent Girin, Simon Leglaive, Xiaoyu Bie +3
Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsu…