https://github.com/cameronking4/clothware
Rendering realistic 3d garments from photo/video
https://github.com/cameronking4/clothware
Last synced: 14 days ago
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Rendering realistic 3d garments from photo/video
- Host: GitHub
- URL: https://github.com/cameronking4/clothware
- Owner: cameronking4
- Created: 2022-11-18T17:37:12.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-11-18T17:45:23.000Z (almost 3 years ago)
- Last Synced: 2025-04-03T05:12:33.980Z (6 months ago)
- Language: Jupyter Notebook
- Size: 1.7 MB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Digital-clothing
This repository contains a code for obtaining a 3D reconstuction of a garment. This is a "proof-of-concept" version of a potential solution for a garment reconstruction task by monocular images. Currently the code supports 2 types of garment ('shirt', 'pants').
For reconstruction you need to have an image of clothing lying on a flat surface. There are 2 variants of usage:
* using front side image of a garment;
* using both: front and back side images.
The reconstruction lies in transfering texture from the image to the corresponding 3D template-mesh of the garment type. The garment type needs to be specified with the input.
# Installation
## Prerequisites
* OS: Ubuntu 20.04.
* [conda](https://docs.conda.io/en/latest/miniconda.html)
* [cuda-toolkit](https://docs.nvidia.com/cuda/index.html#installation-guides)
## Setup
``` shell
conda create -n grecon python=3.7.9
conda activate grecon
# For CUDA 10
# conda install -c pytorch -c conda-forge pytorch=1.10.1 torchvision=0.11.2 cudatoolkit=10.2
# For CUDA 11
conda install -c pytorch -c conda-forge pytorch=1.10.1 torchvision=0.11.2 cudatoolkit=11.1
pip install -r requirements.txt
mkdir input
```
## KDGet installation
``` shell
cd kgdet/deepfashion2_api/PythonAPI
pip install -e .
cd ../../mmdetection
python setup.py develop
cd ../../
```
# Checkpoints and Meshes
Download [checkpoints](https://drive.google.com/file/d/1aBLAkpBHRL39x1ecdlS5rvfMJmCVUGkJ/view?usp=share_link) and [meshes](https://drive.google.com/file/d/18Ln_YN3RAaK9ZmifGhD1mzPhOpGIxQ6K/view?usp=share_link) and unzip them at the root folder.
# Input data
Place a garment images at `./input` folder. You can also use another folder for that purpose, but you will need to specify it for `demo.py` script via `--root` argument.
You can dowload demo-data [here](https://drive.google.com/file/d/11-NJZNpYJv5sWhZxGGNfgS925LqYePQU/view?usp=share_link) and extract it at the root folder.
# Usage
Any of scripts needs to invoked from the root directory. For now there are available 2 possible classes for the reconstruction: `shirt` and `pants`.
Note, that a folder for output needs to be empty, otherwise its content will be removed automatically.
After invoking `demo.py` script check results at the folder specified via `--out` argument.
``` shell
usage: demo.py [-h] [--root ROOT] [--out OUT] [--mesh MESH]
[--checkpoints CHECKPOINTS] --type {pants,shirt} --front FRONT
[--back BACK] [--verbose]
optional arguments:
-h, --help show this help message and exit
--root ROOT Directory, where images are placed.
--out OUT Directory for output results.
--mesh MESH Directory of a folder containing pattern-meshes.
--checkpoints CHECKPOINTS
Directory of a folder containing checkpoints for
kgdet.
--type {pants,shirt} Target type of garment for reconstruction.
--front FRONT Path to image of a front side of a garment.
--back BACK Path to image of a front side of a garment.
```
## Example
```
python demo.py --root ./input --type shirt --front shirt6.jpg --back shirt6_b.jpg
python demo.py --root ./input/pants1 --type pants --front 1.png
python demo.py --root ./input/pants3 --type pants --front 3_f.png --back 3_b.png
```