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README.md

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+# LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching
+
+[Yixun Liang](https://yixunliang.github.io/)$^{\color{red}{\*}}$ [Xin Yang](https://abnervictor.github.io/2023/06/12/Academic-Self-Intro.html)$^{\color{red}{\*}}$, [Jiantao Lin](https://ltt-o.github.io/), [Haodong Li](https://haodong-li.com/), [Xiaogang Xu](https://xiaogang00.github.io), [Yingcong Chen](https://www.yingcong.me)$^{\**}$
+
+$\color{red}{\*}$: Equal contribution.
+\**: Corresponding author.
+
+[Paper PDF (Arxiv)](https://arxiv.org/abs/2311.11284) | [Project Page (Coming Soon)]()
+
 ---
-title: LucidDreamer
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.7.1
-app_file: app.py
-pinned: false
-license: mit
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<div align=center>
+<img src="resources/gif/demo-1.gif" width="47.5%"/><img src="resources/gif/demo-2.gif" width="47.5%"/>  
+
+Note: we compress these motion pictures for faster previewing.
+</div>
+
+<div align=center>
+<img src="resources/teaser.jpg" width="95%"/>  
+  
+Examples of text-to-3D content creations with our framework, the *LucidDreamer*, within **~35mins** on A100.
+</div>
+
+
+## 🎏 Abstract
+
+We present a text-to-3D generation framework, named the *LucidDreamer*, to distill high-fidelity textures and shapes from pretrained 2D diffusion models.
+
+<details><summary>CLICK for the full abstract</summary>
+
+> The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise, they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS, that it brings inconsistent and low-quality updating direction for the 3D model, causing the over-smoothing effect. To address this, we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.
+
+</details>
+
+## 🔧 Training Instructions
+
+Our code is now released! Please refer to this [**link**](resources/Training_Instructions.md) for detailed training instructions.
+
+## 🚧 Todo
+
+- [x] Release the basic training codes
+- [x] Release the guidance documents
+- [ ] Release the training codes for more applications
+
+
+## 📍 Citation 
+```
+@misc{EnVision2023luciddreamer,
+      title={LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching}, 
+      author={Yixun Liang and Xin Yang and Jiantao Lin and Haodong Li and Xiaogang Xu and Yingcong Chen},
+      year={2023},
+      eprint={2311.11284},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+## Acknowledgement
+This work is built on many amazing research works and open-source projects:
+- [gaussian-splatting](https://github.com/graphdeco-inria/gaussian-splatting) and [diff-gaussian-rasterization](https://github.com/graphdeco-inria/diff-gaussian-rasterization)
+- [Stable-Dreamfusion](https://github.com/ashawkey/stable-dreamfusion)
+- [Point-E](https://github.com/openai/point-e)
+
+Thanks for their excellent work and great contribution to 3D generation area.