130 lines
5.2 KiB
Markdown
130 lines
5.2 KiB
Markdown
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# Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis
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[](https://arxiv.org/abs/2412.08603)
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[](https://style3d.github.io/design2garmentcode/)
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[](https://www.youtube.com/xxx)
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<span class="author-block"><a href="">Feng Zhou</a>, </span>
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<span class="author-block"><a href="https://walnut-ree.github.io/">Ruiyang Liu</a>, </span>
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<span class="author-block"><a href="">Chen Liu</a>, </span>
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<span class="author-block"><a href="">Gaofeng He</a>, </span>
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<span class="author-block"><a href="https://dirtyharrylyl.github.io/">Yong-Lu Li</a>, </span>
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<span class="author-block"><a href="http://www.cad.zju.edu.cn/home/jin/">Xiaogang Jin</a>, </span>
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<span class="author-block"><a href="https://wanghmin.github.io/">Huamin Wang</a></span>
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<p align="center">
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<img src="https://github.com/Style3D/design2garmentcode-impl/raw/main/assets/img/neural_symbolic-pipeline.png">
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</p>
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Official implementation for Design2GarmentCode, a motility-agnostic sewing pattern generation framework that leverages fine-tuned Large Multimodal Models to generate parametric pattern-making programs from multi-modal design concepts.
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## Installation
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### 1. Clone the repository
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```bash
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git clone https://github.com/your-org/design2garmentcode.git # ← replace with the real URL
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cd design2garmentcode
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```
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### 2. Create the Conda environment
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An `environment.yml` file is provided in the project root with all required Conda and PyPI dependencies (Python 3.9.19, Torch 2.4.0 + CUDA 12.1, etc.).
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```bash
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conda env create -f environment.yml
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conda activate d2g
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python -m pip install --upgrade pip # optional: upgrade pip
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```
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### 3. (Optional) Enable 3‑D simulation
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If you need local cloth simulation and 3‑D visualization, follow the installation instructions for **GarmentCode Warp Simulator**:
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<https://github.com/maria-korosteleva/NvidiaWarp-GarmentCode>
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---
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### 4. Language‑Model API
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`Design2GarmentCode` communicates with large multimodal models.
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Follow the steps **in the given order**:
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1. **Provide API credentials**
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- **Environment variable (recommended)** – defaults to *ChatGPT‑4o*
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```bash
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export OPENAI_API_KEY="sk‑..."
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```
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- **Edit `system.json`** (project root) – manually specify `api_key`, `base_url`, and `model` if you prefer a file‑based approach.
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2. **Download the required models**:
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- First, download the base model [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/tree/main).
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Place the entire folder at:
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`lmm_utils/Qwen/Qwen2-VL-2B-Instruct/`
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- Next, download the fine-tuned weights file [model.pth](lmm_utils/Qwen/qwen2vl_lora_mlp/model.pth),
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and place it in:
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`lmm_utils/Qwen/qwen2vl_lora_mlp/`
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---
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## Testing with GUI
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Setting up the GUI with `python gui.py` where you will see the following interface (modified from GarmentCode)
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<p align="center">
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<img src="https://github.com/Style3D/design2garmentcode-impl/raw/main/assets/img/gui_example.png">
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</p>
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Switching to the `PARSE DESIGN` tab, and input your design input, either text description, photograph or sketch, to the chatbox. The generated sewing pattern will appear on the right side after parsing.
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Once a pattern is generated, you can modify the result by typing `modify: <your-instruction>` in the chatbox.
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---
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## Batch Inference
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### 1. Text Guided Generation
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Use `test_text_batch.py` to process a list of text descriptions from a JSON file.
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```bash
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python lmm_utils/test_text_batch.py \
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--input assets/test_text/examples.json \
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--output assets/test_text_result \
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--sim
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```
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- `--input`: Path to your input JSON file containing multiple garment description texts.
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- `--output`: Directory where the output `.json` files will be saved.
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- `--sim`: Enable or disable physical simulation output.
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Supports physical simulation (enabled by default in script).
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---
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### 2. Image Guided Generation
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Use `test_picture_batch.py` to process all image files in a directory.
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```bash
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python lmm_utils/test_picture_batch.py \
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--input assets/test_img/examples \
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--output assets/test_image_result/examples \
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--sim
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```
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- `--input`: Folder containing multiple image files.
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- `--output`: Output folder where results will be saved.
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- `--sim`: Enable or disable physical simulation output.
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---
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## Simulate 3D Garment
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### 1. Generate from a pattern.json
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After generating the pattern data, you can simulate the corresponding 3D output directly from the pattern's JSON file with
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```bash
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python test_garment_sim.py --pattern_spec $INPUT_JSON
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```
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Or run the simulation directly in the `3D View` GUI tab.
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### Citation
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```bash
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If you find this work useful, please cite:
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```bibtex
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@article{zhou2024design2garmentcode,
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title={Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis},
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author={Zhou, Feng and Liu, Ruiyang and Liu, Chen and He, Gaofeng and Li, Yong-Lu and Jin, Xiaogang and Wang, Huamin},
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booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
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year={2025}
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}
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``` |