Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction (ICCV 2021 Oral)

This repository contains the code for the paper:

Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction
Fang Zhao, Wenhao Wang, Shengcai Liao and Ling Shao

Requirements

• Cuda 9.2
• Python 3.7
• Pytorch 1.6
• ChamferDistancePytorch

To install all python dependencies for this project:

conda env create -f env_anchor_udf.yml
conda activate AnchorUDF

Testing repo

git clone --recursive git@github.com:osmr/AnchorUDF.git
cd AnchorUDF

pip install --upgrade pip setuptools wheel
pip install -r requirements.txt -f https://download.pytorch.org/whl/cu113/torch_stable.html

python -m apps.eval --results_path=../AnchorUDF_data/results --name=a1 --dataroot=../AnchorUDF_data/data/data_new_1024 --test_folder_path=290-1 --load_netG_checkpoint_path=../AnchorUDF_data/checkpoints/anchor_udf_df3d/netG_epoch_59.zip --anchor --num_steps=5 --filter_val=0.007
python -m apps.eval_hd --results_path=../AnchorUDF_data/results --name=a2 --dataroot=../AnchorUDF_data/data/data_new_1024 --test_folder_path=290-1 --load_netMR_checkpoint_path=../AnchorUDF_data/checkpoints/anchor_udf_hd_df3d/netMR_epoch_14.zip --anchor --merge_layer=2 --joint_train --loadSize=1024 --num_steps=5 --filter_val=0.007
python -m apps.eval_all --results_path=../AnchorUDF_data/results --dataroot=../AnchorUDF_data/data/data_new_1024 --load_netG_checkpoint_path=../AnchorUDF_data/checkpoints/anchor_udf_df3d/netG_epoch_59.zip --anchor --num_steps=5 --filter_val=0.007
python -m apps.compute_errors --results_path=../AnchorUDF_data/results --root_path=../AnchorUDF_data/data/data_new_222

python -m apps.gen_targets --dataroot=../AnchorUDF_data/data/data_new_222 --sigma=0.003 --point_num=600
python -m apps.train --dataroot=../AnchorUDF_data/data/data_new_222 --random_flip --random_scale --random_trans --anchor --learning_rate=5e-5 --batch_size=4 --name=b1 --schedule=40 --num_epoch=50

Dataset

• Deep Fashion3D

We provide the preprocessed data used for model training and evaluation. You can prepare your own data by following the data generation steps of PIFu.

Demo

To do a quick test, download the trained models and run:

python -m apps.eval --results_path {path_of_output} --name {folder_of_output} --dataroot {path_of_dataset} --test_folder_path {folder_of_test_data, e.g., 290-1} --load_netG_checkpoint_path ./checkpoints/anchor_udf_df3d/netG_epoch_59 --anchor --num_steps 5 --filter_val 0.007

For the HD version:

python -m apps.eval_hd --results_path {path_of_output} --name {folder_of_output} --dataroot {path_of_dataset} --test_folder_path {folder_of_test_data, e.g., 290-1} --load_netMR_checkpoint_path ./checkpoints/anchor_udf_hd_df3d/netMR_epoch_14 --anchor --merge_layer 2 --joint_train --loadSize 1024 --num_steps 5 --filter_val 0.007

Optionally, you can remove outliers by statistical outlier removal:

python -m apps.remove_outlier --file_path {path_of_file} --nb_neighbors 5 --std_ratio 10.0

Training

To generate targets for training:

python -m apps.gen_targets --dataroot {path_of_dataset} --sigma {0.003, 0.02, or 0.08} --point_num 600

To train the model:

• First run:

python -m apps.train --dataroot {path_of_dataset} --random_flip --random_scale --random_trans --anchor --learning_rate 5e-5 --batch_size 4 --name {path_of_saved_model} --schedule 40 --num_epoch 50

• Then add gradient direction alignment:

python -m apps.train --dataroot {path_of_dataset} --random_flip --random_scale --random_trans --anchor --learning_rate 5e-6 --batch_size 4 --num_sample_inout 2000 --name {path_of_saved_model} --grad_constraint --backbone_detach --no_num_eval --continue_train --resume_epoch 49 --num_epoch 60

Evaluation

To evaluate the model:

• Obtain reconstruction results on the test set:

python -m apps.eval_all --dataroot {path_of_dataset} --results_path {path_of_output} --load_netG_checkpoint_path {path_of_model} --anchor --num_steps 5 --filter_val 0.007

• Compute Chamfer and P2S errors:

python -m apps.compute_errors --root_path {path_of_dataset} --results_path {path_of_output}

Acknowledgement

Our code is based on PIFu and NDF. We thank the authors for their excellent work!

Citation

If you use this code for your research, please consider citing:

@inproceedings{zhao2021learning,
  title={Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction},
  author={Zhao, Fang and Wang, Wenhao and Liao, Shengcai and Shao, Ling},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12674--12683},
  year={2021}
}