A conditional deep generative model of people in natural images
We propose a deep generative model of humans in natural images which keeps 2D pose separated from other latent factors of variation, such as background scene and clothing. In contrast to methods that learn generative models of low-dimensional representations, e.g., segmentation masks and 2D skeleton...
Hlavní autoři: | De Bem, R, Ghosh, A, Boukhayma, A, Ajanthan, T, Siddharth, N, Torr, P |
---|---|
Médium: | Conference item |
Vydáno: |
IEEE
2019
|
Podobné jednotky
-
DGPose: Deep Generative Models for Human Body Analysis
Autor: de Bem, R, a další
Vydáno: (2020) -
A semi-supervised deep generative model for human body analysis
Autor: De Bem, R, a další
Vydáno: (2019) -
3D hand shape and pose from images in the wild
Autor: Boukhayma, A, a další
Vydáno: (2020) -
Looking deep at people: towards understanding and generating humans in images with deep learning
Autor: de Bem, RA
Vydáno: (2018) -
Cross-modal deep face normals with deactivable skip connections
Autor: Abrevaya, VF, a další
Vydáno: (2020)