127 lines
4.3 KiB
Markdown
127 lines
4.3 KiB
Markdown
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# Design2GarmentCode: Programmatic Garment Patterns from Text and Images
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[arXiv](https://arxiv.org/abs/2412.08603) | [Project Page](https://style3d.github.io/design2garmentcode/)
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Feng Zhou, Ruiyang Liu, Chen Liu, Gaofeng He, Yong‑Lu Li, Xiaogang Jin, Huamin Wang. *CVPR 2025 .*
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we propose a novel
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sewing pattern generation approach Design2GarmentCode
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based on Large Multimodal Models (LMMs), to generate parametric pattern-making programs from multi-modal
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design concepts
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---
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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. **You should first download the base model**
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[`Qwen2-VL-2B-Instruct`]<https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/tree/main>
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and place its full folder in:
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`lmm_utils/Qwen/Qwen2-VL-2B-Instruct/`
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Then make sure that [`model.pth`]<lmm_utils/Qwen/qwen2vl_lora_mlp/model.pth> is placed in the `lmm_utils/Qwen/qwen2vl_lora_mlp/` directory.
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This file contains the LoRA+MLP fine-tuned weights.
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---
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## Quick GUI Demo
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```bash
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python gui.py
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```
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- Input: free‑form prompt or an image/sketch
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- Output: GarmentCode JSON, preview image, and (optionally) physics simulation
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---
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## Model 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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### 3. Modify Patterns in the GUI
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Once a pattern is generated in GUI, you can refine them directly inside the GUI:
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1. Focus the **input box** at the bottom.
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2. Type `modify: <your-instruction>` .
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3. Press **Enter** – the system will regenerate the pattern to reflect your changes.
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## Get 3D Garment Patterns
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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.
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```bash
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python lmm_utils/test_garment_sim.py --pattern_spec $OUTPUT_JSON
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```
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### 2. Generate from gui
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You can also run the simulation directly on the GUI to obtain 3D data.
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```bash
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python gui.py
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```
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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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journal={arXiv preprint arXiv:2412.08603},
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year={2024}
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}
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``` |