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High-fidelity 3D Face Generation from Natural Language Descriptions (CVPR 2023)

This is the official repository of ''High-fidelity 3D Face Generation from Natural Language Descriptions'', CVPR 2023. [Project Page] [arXiv]

Our pipeline

Visual Results

[Updates]

• 2023.05.04: Release the inference code and pre-trained model.

[To do list]

• Release Describe3D Dataset

Getting started

Requirements

• Python = 3.8
• pytorch = 1.7.1
• cudatoolkit = 11.0

Configure the environment

1. First, you need to build the virtual environment.

conda create -n describe3d python=3.8

2. Then, you need to install CLIP. Please refer to https://github.com/openai/CLIP
3. Install other dependencies.

pip install -r requirements.txt

Usage

1. Download the pre-trained texture generation model and put it into the checkpoints/texture_synthesis/

https://drive.google.com/drive/folders/1zqCLaF-KzhWy_YSMqKf15aEKiv19lXz5?usp=sharing

2. Then you can run the main.py to generate 3D Faces.

python main.py --name="your model name" --descriptions="the description of the face you want to generate." --prompt="the abstract descriptions"

Here is an example.

python main.py --name="Stark" --descriptions="This middle-aged man is a westerner. He has big and black eyes with the double eyelid. He has a medium-sized nose with a high nose bridge. His face is square and medium. He has a dense and black beard." --prompt="Tony Stark." --lambda_latent=0.0003 --lambda_param=3

The abstract synthesis results are affected by the regularization coefficient. Decrease lambda_param / lambda_latent when you want more changes of shape / texture. On the contrary, you can increase them.