batermj / ghfeat

Code for paper `Generative Hierarchical Features from Synthesizing Images`

GH-Feat - Generative Hierarchical Features from Synthesizing Images

Figure: Training framework of GH-Feat.

Generative Hierarchical Features from Synthesizing Images
Yinghao Xu*, Yujun Shen*, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou
arXiv preprint arXiv:2007.10379

[Paper] [Project Page]

In this repository, we show that well-trained GAN generators can be used as training supervision to learn hierarchical and disentangled visual features. We call this feature as Generative Hierarchical Feature (GH-Feat). Properly learned from a novel hierarchical encoder, GH-Feat is able to facilitate both discriminative and generative visual tasks, including face verification, landmark detection, layout prediction, transfer learning, style mixing, and image editing, etc. Some results are shown as follows.

Discriminative Tasks

Indoor scene layout prediction

Facial landmark detection

Face verification (face reconstruction)

Generative Tasks

Image harmonization

Global editing

Local Editing

Multi-level style mixing

BibTeX

@article{xu2020generative,
  title   = {Generative Hierarchical Features from Synthesizing Images},
  author  = {Xu, Yinghao and Shen, Yujun and Zhu, Jiapeng and Yang, Ceyuan and Zhou, Bolei},
  journal = {arXiv preprint arXiv:2007.10379},
  year    = {2020}
}

Code Coming Soon