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https://github.com/sail-sg/sewformer

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Host: GitHub
URL: https://github.com/sail-sg/sewformer
Owner: sail-sg
Created: 2023-11-07T11:58:25.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2023-12-28T09:03:16.000Z (11 months ago)
Last Synced: 2024-08-02T14:10:54.173Z (4 months ago)
Language: Python
Size: 1.83 MB
Stars: 130
Watchers: 19
Forks: 13
Open Issues: 10
Metadata Files:
- Readme: ReadMe.md

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README

        # Sewformer

This is the official implementation of [Towards Garment Sewing Pattern Reconstruction from a Single Image](https://arxiv.org/abs/2311.04218v1).

[Lijuan Liu](https://scholar.google.com/citations?user=nANxp5wAAAAJ&hl=en)^*,

[Xiangyu Xu](https://xuxy09.github.io/)^*,

[Zhijie Lin](https://scholar.google.com/citations?user=xXMj6_EAAAAJ&hl=zh-CN)^*,

[Jiabing Liang]()^*,

[Shuicheng Yan](https://yanshuicheng.info/)^{†,  

ACM Transactions on Graphics (SIGGRAPH Asia 2023)}

### [Project](https://sewformer.github.io/) | [Paper](https://arxiv.org/abs/2311.04218v1)



---------------------------

### Installation and Configuration

* Clone this repository to `path_to_dev` and `cd path_to_dev/Sewformer`, download the pre-trained [checkpoint](https://huggingface.co/liulj/sewformer) and put it into `assets/ckpts`.

* The environment can be initialized with `conda env create -f environment.yaml`. Then you can activate the environment `conda activate garment`. 

### Training

* Download our provided [dataset](https://huggingface.co/datasets/liulj/sewfactory) and put it into `path_to_sewfactory`, update the local paths in `system.json` to make sure the dataset setup correctly. 

* Train the model with

`torchrun --standalone --nnodes=1 --nproc_per_node=1 train.py -c configs/train.yaml`

  The output will be located at the `output` in `system.json`.

### Testing

1. Inference sewing patterns with the pretrained model: 

* evaluate on sewfactory dataset: `torchrun --standalone --nnodes=1 --nproc_per_node=1 train.py -c configs/train.yaml -t`

* inference on real images (e.g. from deepfashion):

    `python inference.py -c configs/test.yaml -d assets/data/deepfashion -t deepfashion -o outputs/deepfashion` 

2. Simulate the predicted results (Windows):

`cd path_to_dev/SewFactory` and run `path_to_maya\bin\mayapy.exe .\data_generator\deepfashion_sim.py` to simulate the predicted sew patterns. (Please prepare the SMPL prediction results with [RSC-Net](https://github.com/xuxy09/RSC-Net) and update the predicted data root specified in `deepfashion_sim.py`.)

    See more details about the SewFactory dataset and the simulation [here](./SewFactory/ReadMe.md).

### BibTex

Please cite this paper if you find the code/model helpful in your research:

```

 @article{liu2023sewformer,

    author      = {Liu, Lijuan and Xu, Xiangyu and Lin, Zhijie and Liang, Jiabin and Yan, Shuicheng},

    title       = {Towards Garment Sewing Pattern Reconstruction from a Single Image},

    journal     = {ACM Transactions on Graphics (SIGGRAPH Asia)},

    year        = {2023}

  }

```