En3D - Official PyTorch Implementation
Project page | Paper | Video
En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data
Yifang Men,
Biwen Lei,
Yuan Yao,
Miaomiao Cui,
Zhouhui Lian,
Xuansong Xie
En3D is a large 3D human generative model trained on millions of synthetic 2D data, independently of any pre-existing 3D or 2D assets. This repo contains an implementation of En3D and provides a series of applications built upon it. In addition, this repo aims to be a useful creative tool to produce realistic 3D avatars from seeds, text prompts or images, and support automatic character animation FBX production. All outputs are compatible with the modern graphics workflows.
Generative 3D humans
gallery.mp4
Text guided synthesis
Image guided synthesis
More results can be found in project page.
Updates
(2023-01-03) The paper and video are released.
(2023-12-20) The project page is available now at website.
Citation
If you find this code useful for your research, please use the following BibTeX entry.
@inproceedings{men2024en3d,
title={En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data},
author={Men, Yifang and Lei, Biwen and Yao, Yuan and Cui, Miaomiao and Lian, Zhouhui and Xie, Xuansong},
journal={arXiv preprint arXiv:2401.01173},
website={https://menyifang.github.io/projects/En3D/index.html},
year={2024}}