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...
Main Authors: | De Bem, R, Ghosh, A, Boukhayma, A, Ajanthan, T, Siddharth, N, Torr, P |
---|---|
Formato: | Conference item |
Publicado em: |
IEEE
2019
|
Registos relacionados
-
DGPose: Deep Generative Models for Human Body Analysis
Por: de Bem, R, et al.
Publicado em: (2020) -
A semi-supervised deep generative model for human body analysis
Por: De Bem, R, et al.
Publicado em: (2019) -
3D hand shape and pose from images in the wild
Por: Boukhayma, A, et al.
Publicado em: (2020) -
Looking deep at people: towards understanding and generating humans in images with deep learning
Por: de Bem, RA
Publicado em: (2018) -
Cross-modal deep face normals with deactivable skip connections
Por: Abrevaya, VF, et al.
Publicado em: (2020)