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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 <br>Fang Zhao, Wenhao Wang, Shengcai Liao and Ling Shao <br>

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

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, AND 0.08, separately} --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}
}