The main contributions of our human image systhesis model are listed as followed:

1. The first method to synthesize semantic disentangled full-body human image.
2. We propose a 3D-aware super-resolution module which enable our project achieve 3D-aware image synthesis at $1024^2$ resolution.

• Training code
• Datasets prepocessing code
• Code for more applications

• We finished our training and testing tasks on 4 NVIDIA A40 GPUs.
• 64-bit Python 3.8 and PyTorch 1.11.0 (or later). See https://pytorch.org for PyTorch install instructions.
• CUDA toolkit 11.3 or later.
• pytorch3d. See https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md for install instructions.

Clone this repo:

git clone https://github.com/Alfonsoever/SemanticHuman-HD.git

cd SemanticHuman-HD

We suggest use anaconda to manage the python environment:

conda env create -f environment.yml
conda activate HD
python setup.py install

sh ./scripts/download_model.sh

Download SMPL models (1.0.0 for Python 2.7 (10 shape PCs)) and move them to the corresponding locations:

mkdir training/deformers/smplx/SMPLX
mv /path/to/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl training/deformers/smplx/SMPLX/SMPL_NEUTRAL.pkl

sh ./scripts/test.sh

sh ./scripts/fid.sh

To see more details,please go to our project page.