

Dresscode
Overview :
DressCode is a text-driven framework for 3D clothing generation, designed to democratize design for beginners and unleash tremendous potential for fashion design, virtual fitting, and digital human creation. It initially introduces SewingGPT, a GPT-based architecture that integrates cross-attention and text-conditioned embeddings for generating sewing patterns from text instructions. Next, it customizes a pre-trained Stable Diffusion to create physically based rendering (PBR) textures based on tiles. By leveraging large language models, this framework generates CG-friendly clothing through natural language interactions, facilitates pattern completion and texture editing, and simplifies the design process through a user-friendly interface.
Target Users :
DressCode is ideal for designers, fashion enthusiasts, and digital content creators because it provides an intuitive and user-friendly way to generate and customize 3D clothing without needing in-depth technical knowledge.
Use Cases
Designers rapidly generate clothing sketches and textures with DressCode, accelerating the design process.
Fashion brands offer virtual fitting services through DressCode, enhancing customer shopping experiences.
Digital content creators use DressCode to design outfits for virtual characters, enriching character portrayals.
Features
Generate clothing patterns and textures through natural language interaction.
Support pattern completion and texture editing, simplifying the design process.
Utilize large language models to create CG-friendly clothing.
Foster innovation and design experimentation via a user-friendly interface.
Provide high-quality rendered results aligned with input prompts.
Support subsequent simulation and animation in 3D clothing design software.
How to Use
1. Clone or download the DressCode project to your local environment.
2. Set environment variables and update local path configurations based on the project documentation.
3. Download and install the necessary dependencies, such as Stable Diffusion 2-1.
4. Train the SewingGPT model using the provided scripts and datasets.
5. Test the pre-trained model using the UI interface or command line tools, input textual prompts to generate clothing patterns and textures.
6. Utilize the generated 3D patterns and textures for further simulation and animation in 3D clothing design software.
7. Edit textures through the UI interface to customize clothing textures as needed.
Featured AI Tools
Chinese Picks

Capcut Dreamina
CapCut Dreamina is an AIGC tool under Douyin. Users can generate creative images based on text content, supporting image resizing, aspect ratio adjustment, and template type selection. It will be used for content creation in Douyin's text or short videos in the future to enrich Douyin's AI creation content library.
AI image generation
9.0M

Outfit Anyone
Outfit Anyone is an ultra-high quality virtual try-on product that allows users to try different fashion styles without physically trying on clothes. Using a two-stream conditional diffusion model, Outfit Anyone can flexibly handle clothing deformation, generating more realistic results. It boasts extensibility, allowing adjustments for poses and body shapes, making it suitable for images ranging from anime characters to real people. Outfit Anyone's performance across various scenarios highlights its practicality and readiness for real-world applications.
AI image generation
5.3M