update arxiv link

app.py

@@ -137,7 +137,7 @@ _HEADER_ = '''
 This is official demo for our technical report <a href="">DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation </a>.
 **DI-PCG** is a diffusion model which directly generates a procedural generator's parameters from a single image, resulting in high-quality 3D meshes.
 
-Code: <a href='https://github.com/TencentARC/DI-PCG' target='_blank'>GitHub</a>. Techenical report: <a href='' target='_blank'>ArXiv</a>.
+Code: <a href='https://github.com/TencentARC/DI-PCG' target='_blank'>GitHub</a>. Techenical report: <a href='http://arxiv.org/abs/2412.15200' target='_blank'>ArXiv</a>.
 
 ❗️❗️❗️**Important Notes:**
 - DI-PCG trains a diffusion model for each procedural generator. Current supported generators are: Chair, Table, Vase, Basket, Flower, Dandelion from <a href="https://github.com/princeton-vl/infinigen">Infinigen</a>.
@@ -151,7 +151,12 @@ If DI-PCG is helpful, please help to ⭐ the <a href='https://github.com/Tencent
 
 If you find our work useful for your research or applications, please cite using this bibtex:
 ```bibtex
-
+@article{zhao2024dipcg,
+  title={DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation},
+  author={Zhao, Wang and Cao, Yanpei and Xu, Jiale and Dong, Yuejiang and Shan, Ying},
+  journal={arXiv preprint arXiv:2412.15200},
+  year={2024}
+}
 ```
 
 📋 **License**