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...
主要な著者: | De Bem, R, Ghosh, A, Boukhayma, A, Ajanthan, T, Siddharth, N, Torr, P |
---|---|
フォーマット: | Conference item |
出版事項: |
IEEE
2019
|
類似資料
-
DGPose: Deep Generative Models for Human Body Analysis
著者:: de Bem, R, 等
出版事項: (2020) -
A semi-supervised deep generative model for human body analysis
著者:: De Bem, R, 等
出版事項: (2019) -
3D hand shape and pose from images in the wild
著者:: Boukhayma, A, 等
出版事項: (2020) -
Looking deep at people: towards understanding and generating humans in images with deep learning
著者:: de Bem, RA
出版事項: (2018) -
Cross-modal deep face normals with deactivable skip connections
著者:: Abrevaya, VF, 等
出版事項: (2020)