From 891dbb64e79b38dc4ef9ba2e189e7d2b1f3cedda Mon Sep 17 00:00:00 2001 From: freemty Date: Tue, 31 Dec 2024 14:47:16 +0800 Subject: [PATCH] add readme --- README.md | 47 ++++++++++++++++++++++++++++++++++++-- docs/assets/method.jpg | Bin 0 -> 837930 bytes docs/assets/teaser_p1.jpg | Bin 0 -> 2918528 bytes 3 files changed, 45 insertions(+), 2 deletions(-) create mode 100644 docs/assets/method.jpg create mode 100644 docs/assets/teaser_p1.jpg diff --git a/README.md b/README.md index 214a746..86316fa 100644 --- a/README.md +++ b/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 + + + +[![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) + + +> **Prometheus🔥: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation**
+> Yuanbo Yang, Jiahao Shao and Xinyang, Li and Yujun, Shen and Andreas, Geiger and Yiyi, Liao
+ + + +**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. + + + +**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}, +} +``` \ No newline at end of file diff --git a/docs/assets/method.jpg b/docs/assets/method.jpg new file mode 100644 index 0000000000000000000000000000000000000000..257dff46308f74014d2121ec3b06367254c0fcf9 GIT binary patch literal 837930 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