update 3DPortraitGAN

_publications/3DPortraitGAN.md

@@ -5,8 +5,8 @@ permalink: /publication/3DPortraitGAN
 excerpt: '**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)'
 date: 2023-08-22
 venue: 'Preprints'
-paperurl: 'coming soon'
-citation: 'coming soon'
+paperurl: 'https://arxiv.org/abs/2307.14770'
+citation: 'http://oneThousand1000.github.io/files/bibtex/3DPortraitGAN.html'
 code: 'https://github.com/oneThousand1000/3DPortraitGAN'
 video: 'coming soon'
 supplementary_materials: 'https://drive.google.com/file/d/16aNE5USZ0U32bgGJS1G5xWrY0oIMTfre/view?usp=sharing'
@@ -19,9 +19,7 @@ year: '2023'
 
 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](https://www.backstage.com/magazine/article/types-of-headshots-75557/) 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.  
 
-[Paper]() 
-
-[Video]() 
+[Paper](https://arxiv.org/abs/2307.14770) 
 
 [Suppl](https://drive.google.com/file/d/16aNE5USZ0U32bgGJS1G5xWrY0oIMTfre/view?usp=sharing) 
 
@@ -31,5 +29,12 @@ year: '2023'
 
 Recommended citation: 
 ```
-coming soon
+@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}
+}
 ```

files/bibtex/3DPortraitGAN.html

@@ -0,0 +1,10 @@
+
+
+<p>@misc{wu20233dportraitgan,<br>
+      title={3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses}, <br>
+      author={Yiqian Wu and Hao Xu and Xiangjun Tang and Hongbo Fu and Xiaogang Jin},<br>
+      year={2023},<br>
+      eprint={2307.14770},<br>
+      archivePrefix={arXiv},<br>
+      primaryClass={cs.CV}<br>
+}<p>