#Text-to-3D

Discover techniques for converting text and images into 3D models using advanced 2D priors such as stable diffusion and CLIP. This repository showcases various methods including zero-shot text-guided generation and multi-view consistent image transformation, presenting developments since 2022. As research progresses, directly training on 3D data models offers promising results, with comprehensive listings of the latest advancements in text and image-guided 3D creation.

GaussianDreamerPro enhances 3D Gaussian generation by integrating defined geometries. This framework offers detailed 3D assets suitable for animation and simulation, maintaining controlled Gaussian growth for improved quality and usability across various processes.

This project offers a detailed curation of Text-to-3D and Diffusion-to-3D methods inspired by awesome-NeRF, featuring numerous research papers on converting text to 3D models using techniques like Neural Radiance Fields (NeRF) and advanced diffusion models. It is regularly updated with new resources including project page links, video tutorials, and cited literature with code repositories, providing a valuable resource for exploring innovative 3D synthesis developments. Applications range from object generation to novel view synthesis, serving as an informative guide for researchers and enthusiasts.

RichDreamer applies a Normal-Depth Diffusion Model to convert text into detailed 3D images, suitable for various applications. The model, accepted at CVPR2024, supports MultiView-ND and Albedo Diffusion for enhanced detail. It includes installation instructions and pre-trained weights, and works efficiently with various GPUs. Resources are available on ModelScope's 3D Object Generation platform.