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ftbabi / LayersNet_ICCV2023

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Towards Multi-Layered 3D Garments Animation

[Paper] | [Project] | [Dataset]

This is the official repository of "Towards Multi-Layered 3D Garments Animation, ICCV 2023".

Authors: Yidi Shao, Chen Change Loy, and Bo Dai.

Acknowedgement: This study is supported under the RIE2020 Industry Alignment Fund Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). It is also supported by Singapore MOE AcRF Tier 2 (MOE-T2EP20221-0011) and Shanghai AI Laboratory.

Feel free to ask questions. I am currently working on some other stuff but will try my best to reply. Please don't hesitate to star!

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Table of Content

  1. Video Demos
  2. Dataset
  3. Code
  4. Citations

Video Demos

Please refer to our project page for more details.

Dataset

Please follow this repo to download and prepare the dataset.

Code

Codes are tested on Ubuntu 18 and cuda 11.3. We train our model with 4 V100.

Installation

  1. Create a conda environment with necessary packages 
    
# PyTorch 1.10
conda create -n LayersNet python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 torchaudio==0.10.0 -c pytorch -y
conda activate LayersNet

mmcv-full

pip3 install openmim mim install mmcv-full==1.6.1

Other dependent packages

pip3 install h5py pyrender trimesh numpy==1.23.1 tqdm plotly scipy chumpy matplotlib

2. Clone and install this repo

git clone https://github.com/ftbabi/LayersNet_ICCV2023.git

cd LayersNet_ICCV2023 pip3 install -v -e .


### Dataset Preparation
1. Download [LAYERS](https://github.com/ftbabi/D-LAYERS_ICCV2023.git) and create a soft link to [LAYERS](https://github.com/ftbabi/D-LAYERS_ICCV2023.git) dataset.

ln -s PATH/TO/LAYERS/ data

2. To generate data for this repo, use the following commands

Prepare the static information

python tools/preprocessing_data.py configs/layersnet/base/ep1.py --work_dir PATH/TO/LOG/DIR --dataset [train/val/test] --type static

Prepare the dynamic information, e.g., velocity

python tools/preprocessing_data.py configs/layersnet/base/ep1.py --work_dir PATH/TO/LOG/DIR --dataset [train/val/test] --type dynamic


### Training
Train on multiple GPUs

sh tools/dist_seq.sh PATH/TO/CONFIG/DIR/ NUM_GPUS PATH/TO/WORK/DIR/ --seed 0


### Testing
1. Rollout the results of one sequence, e.g., sequence `00396` here:

python tools/test.py PATH/TO/CONFIG/FILE PATH/TO/CHECKPOINT --show-dir PATH/TO/SAVE/DIR/ --show-options rollout=396

2. Save quantitative results to a json file, e.g., `eval.json` here:

python tools/test.py PATH/TO/CONFIG/FILE PATH/TO/CHECKPOINT --out PATH/TO/SAVE/eval.json


### Visualization
To visualize the output, here take 10th frame from sequence `00396` as example, please use the following command

python tools/visualization.py PATH/TO/CONFIG/FILE PATH/TO/ROLLOUT/DIR/ --seq 396 --frame 10

Please note that the `PATH/TO/ROLLOUT/DIR/` should be the same as `PATH/TO/SAVE/DIR/` in [Testing Section](#testing).

## Citations

@inproceedings{shao2023layersnet, author = {Shao, Yidi and Loy, Chen Change and Dai, Bo}, title = {Towards Multi-Layered {3D} Garments Animation}, booktitle = {ICCV}, year = {2023} }