3DPortraitGAN.md

title	collection	permalink	excerpt	date	venue	paperurl	citation	code	video	supplementary_materials	project_page	year	first_author	teaser
3DPortraitGAN: Learning Canonical Full-Head 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses	publications	/publication/3DPortraitGAN	**Yiqian Wu**, [Hao Xu](https://xh38.github.io/), [Xiangjun Tang](https://yuyujunjun.github.io/), [Hongbo Fu](http://sweb.cityu.edu.hk/hongbofu/publications.html), [Xiaogang Jin*](http://www.cad.zju.edu.cn/home/jin)	2023-08-22	Preprints	https://arxiv.org/abs/2307.14770	http://oneThousand1000.github.io/files/bibtex/3DPortraitGAN.html	https://github.com/oneThousand1000/3DPortraitGAN	coming soon	https://drive.google.com/file/d/16aNE5USZ0U32bgGJS1G5xWrY0oIMTfre/view?usp=sharing	https://onethousandwu.com/3DPortraitGAN.github.io/	2023	yes	http://oneThousand1000.github.io/images/publications/3DPortraitGAN.png

[Preprint] 3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

Yiqian Wu, Hao Xu, Xiangjun Tang, Hongbo Fu, Xiaogang Jin*

Abstract:

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360°-Portrait-HQ (360°PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360° range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360°PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles.

Recommended citation:

@misc{wu20233dportraitgan,
      title={3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses}, 
      author={Yiqian Wu and Hao Xu and Xiangjun Tang and Hongbo Fu and Xiaogang Jin},
      year={2023},
      eprint={2307.14770},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}