| title | collection | permalink | excerpt | date | venue | paperurl | citation | code | video | supplementary_materials | project_page | year |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
coming soon |
2023 |
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}
}