add readme

README.md

@@ -1,2 +1,45 @@
-# Prometheus
-Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation
+# Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation
+
+<!-- 
+[Jiahao Shao*](https://jhaoshao.github.io/), Yuanbo Yang*, Hongyu Zhou, [Youmin Zhang](https://youmi-zym.github.io/),  [Yujun Shen](https://shenyujun.github.io/), [Vitor Guizilini](https://vitorguizilini.github.io/), [Yue Wang](https://yuewang.xyz/), [Matteo Poggi](https://mattpoggi.github.io/), [Yiyi Liao](https://yiyiliao.github.io/ ) -->
+
+[![Website](https://img.shields.io/website?url=https%3A%2F%2Fjhaoshao.github.io%2FChronoDepth%2F&up_message=ChronoDepth&up_color=blue&style=flat&logo=timescale&logoColor=%23FFDC0F)](https://freemty.github.io/project-prometheus/) [![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://arxiv.org/pdf/2412.21117)
+
+ <!-- [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/jhshao/ChronoDepth)
+[![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/jhshao/ChronoDepth-v1) -->
+> **Prometheus🔥: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation** <br>
+> Yuanbo Yang, Jiahao Shao and Xinyang, Li and Yujun, Shen and Andreas, Geiger and Yiyi, Liao <br>
+
+<img src="./docs/assets/teaser_p1.jpg"/>
+
+**Overview:** *We present a novel method for feed-forward scene-level 3D generation. At its core, our approach harnesses the power of 2D priors to fuel generalizable and efficient 3D synthesis – hence our name, Prometheus🔥*
+
+
+In this work, we introduce Prometheus🔥, a 3D-aware latent diffusion model for text-to-3D generation at both object and scene levels in seconds. We formulate 3D scene generation as multi-view, feed-forward, pixel-aligned 3D Gaussian generation within the latent diffusion paradigm. To ensure generalizability, we build our model upon pre-trained text-to-image generation model with only minimal adjustments, and further train it using a large number of images from both single-view and multi-view datasets. Furthermore, we introduce an RGB-D latent space into 3D Gaussian generation to disentangle appearance and geometry information, enabling efficient feed-forward generation of 3D Gaussians with better fidelity and geometry. Extensive experimental results demonstrate the effectiveness of our method in both feed-forward 3D Gaussian reconstruction and text-to-3D generation.
+
+<img src="./docs/assets/method.jpg"/>
+
+**Method:** *Our training process is divided into two stages. In stage 1, our objective is to train a GS-VAE. Utilizing multi-view images along with their corresponding pseudo depth maps and camera poses, our GS-VAE is designed to encode these multi-view RGB-D images, integrate cross-view information, and ultimately decode them into pixel-aligned 3DGS. In stage 2, we focus on training a MV-LDM. We can generate multi-view RGB-D latents by sampling from randomly-sampled noise with trained MV-LDM.*
+
+
+## 📢 News
+2024-12-21: Project Page and 
+
+
+## ✅ TODO
+- [ ] Release inference code and checkpoint (Before 2025-01-10)
+- [ ] Set up Online demo for inference (Before 2025-01-20)
+- [ ] Release training code & dataset preparation
+
+## 🎓 Citation
+
+Please cite our paper if you find this repository useful:
+
+```bibtex
+@article{yang2024prometheus,
+      title={Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation}, 
+      author={Yuanbo, Yang and Jiahao, Shao and Xinyang, Li and Yujun, Shen and Andreas, Geiger and Yiyi, Liao},
+      year={2024},
+      journal= {arxiv:2412.21117},
+}
+```