Adding diffusion model to Pamir.

Please run the following commands to download necessary assets (including the pre-trained models):

cd ./networks
wget https://github.com/ZhengZerong/PaMIR/releases/download/v0.0/results.zip
unzip -o results.zip
cd ..

After that, run the following script to test the pre-trained network:

cd ./networks
python main_test.py
cd ..

This command will generate the textured reconstruction with the fitted SMPLs for the example input images in ./network/results/test_data*/. Note that we assume the input images are tightly cropped with the background removed and the height of the persons is about 80% of the image height (Please see the example input images we provide).

In dataset_example, we provide an example data item, which contains a textured mesh and a SMPL model fitted to the mesh. The mesh is downloaded from RenderPeople. To generate the training images, please run:

cd ./data
python main_normalize_mesh.py                         # we normalize all scans into a unit bounding box
python main_calc_prt.py
python main_render_images.py
python main_sample_occ.py
python main_associate_points_with_smpl_vertices.py    # requires SMPL fitting
cd ..

Note that the last python script requires SMPL model fitted to the scans. To fit SMPL to your own 3D scans, you can use our tool released at this link.

Please run the following command to train the network:

cd ./networks
bash ./scripts/train_script_geo.sh  # geometry network
bash ./scripts/train_script_tex.sh  # texture network
cd ..