update

_pages/about.md

@@ -62,3 +62,7 @@ I'm Yiqian Wu (吴奕谦), a second-year (2021-now) Ph.D. student in State Key L
    ACM Trans. Graph. 40, 6, Article 217 (December 2021), 17 pages.
 
    Qinjie Xiao, Hanyuan Zhang, Zhaorui Zhang, **Yiqian Wu**, Luyuan Wang, Xiaogang Jin, Xinwei Jiang, Yongliang Yang, Tianjia Shao, Kun Zhou.
+
+## 2020
+
+1. 

_pages/publications.md

@@ -19,6 +19,8 @@ author_profile: true
   {% endif %}
 {% endfor %}
 
+
+
 ## 2022
 
 {% for post in site.publications reversed %}
@@ -27,10 +29,22 @@ author_profile: true
   {% endif %}
 {% endfor %}
 
+
+
 ## 2021
 
 {% for post in site.publications reversed %}
   {% if post.year == '2021' %}
       {% include archive-single.html %}
   {% endif %}
 {% endfor %}
+
+
+
+## 2020
+
+{% for post in site.publications reversed %}
+  {% if post.year == '2020' %}
+      {% include archive-single.html %}
+  {% endif %}
+{% endfor %}

_publications/iothopredictor.md

@@ -0,0 +1,22 @@
+---
+title: "iOrthoPredictor: Model-guided Deep Prediction of Teeth Alignment"
+collection: publications
+permalink: /publication/iothopredictor
+excerpt: 'Lingchen Yang, Zefeng Shi, **Yiqian Wu**, Xiang Li, Kun Zhou, Hongbo Fu, Youyi Zheng.'
+date: 2020-12-30
+venue: 'ACM Transactions on Graphics (Proc. of Siggraph Asia 2020), 39(6), Article 216.'
+paperurl: 'https://dl.acm.org/doi/10.1145/3414685.3417771'
+citation: 'http://oneThousand1000.github.io/files/bibtex/iothopredictor.html'
+year: '2020'
+---
+![iothopredictor](http://oneThousand1000.github.io/images/publications/iothopredictor.png)
+
+<b>Abstract:</b>
+In this paper, we present iOrthoPredictor, a novel system to visually predict teeth alignment in photographs. Our system takes a frontal face image of a patient with visible malpositioned teeth along with a corresponding 3D teeth model as input, and generates a facial image with aligned teeth, simulating a real orthodontic treatment effect. The key enabler of our method is an effective disentanglement of an explicit representation of the teeth geometry from the in-mouth appearance, where the accuracy of teeth geometry transformation is ensured by the 3D teeth model while the in-mouth appearance is modeled as a latent variable. The disentanglement enables us to achieve fine-scale geometry control over the alignment while retaining the original teeth appearance attributes and lighting conditions. The whole pipeline consists of three deep neural networks: a U-Net architecture to explicitly extract the 2D teeth silhouette maps representing the teeth geometry in the input photo, a novel multilayer perceptron (MLP) based network to predict the aligned 3D teeth model, and an encoder-decoder based generative model to synthesize the in-mouth appearance conditional on the original teeth appearance and the aligned teeth geometry. Extensive experimental results and a user study demonstrate that iOrthoPredictor is effective in qualitatively predicting teeth alignment, and applicable to the orthodontic industry. 
+
+
+
+
+[[pdf]](https://dl.acm.org/doi/10.1145/3414685.3417771)
+
+Recommended citation: [[bibtex]](http://oneThousand1000.github.io/files/bibtex/iothopredictor.html)