Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation

Yuanbo Yang, Jiahao Shao, Xinyang Li, Yujun Shen, Andreas Geiger +1 more
2/9/2026

No official code found. Best implementation: XDimLab/Prometheus (147 stars, MIT, last commit 2025-07-05).

Abstract

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 pretrained 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. Project page: Prometheus.

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Code Implementations(1)

XDimLab/PrometheusCommunity78%
MIT

[CVPR2025] Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation

1477Shell, PythonDec 17, 20241 years agoMIT

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Cite this paper

@article{yang2026prometheus,
  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 Yi Liao},
  year   = {2026},
  eprint = {2412.21117},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2412.21117},
  journal = {CVPR 2024 2024}
}

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