AI-ClothingTryOn – An AI virtual try-on application that supports generating multiple versions of dressing effects.

What is AI-ClothingTryOn?

AI-ClothingTryOn is a desktop application based on Python, leveraging Google Gemini AI technology to achieve virtual try-on functionality. AI-ClothingTryOn supports uploading separate photos of people and clothing, and uses AI technology to generate realistic composite images that showcase the effect of the person wearing the selected clothing. AI-ClothingTryOn allows users to generate multiple versions of try-on effects and customize the results by providing their own AI prompts. Suitable for both ordinary users and developers, AI-ClothingTryOn offers two installation options: an EXE file and source code, catering to the needs of different user groups.

The main functions of AI-ClothingTryOn

• Photo Upload Function: Supports uploading portrait photos and clothing photos separately.
• Multi-Version Try-On Effect Generation: Generates up to 10 different try-on effect versions, providing a variety of choices.
• Custom AI Prompt Words: Supports users in adjusting AI prompt words to optimize the generation results and meet personalized needs.
• Batch Processing Support: Supports processing multiple photos simultaneously to improve work efficiency.

The Technical Principle of AI-ClothingTryOn

• Image Segmentation and Extraction: Segment person photos based on an AI model to extract the body contours and key parts of the human body, and identify and extract clothing photos.
• Image Fusion and Synthesis: Leverage the generative AI capabilities of Google Gemini to fuse the extracted human contours with clothing images, generating realistic try-on effects. Gemini AI utilizes deep learning models trained on a vast amount of image data to produce high-quality, lifelike synthetic images.
• Multi-Version Generation: Adjust AI prompts and parameters to generate multiple variations of try-on effects, meeting users’ diverse needs.
• Multi-thread Processing: Implement multi-thread processing based on Threading technology to support simultaneous handling of multiple images, improving the program’s operational efficiency.
• User Interface Interaction: Build a graphical user interface using PyQt6 to provide a user-friendly experience, making it easy for users to upload images, adjust parameters, and save results.

Project address of AI-ClothingTryOn

• GitHub Repository: https://github.com/speedTD/AI-ClothingTryOn

Application scenarios of AI-ClothingTryOn

• Online Shopping: Help consumers try on clothes in advance to reduce returns.
• Fashion Design: Quickly display design effects for easy adjustment.
• Matching Recommendations: Provide personalized matching suggestions to optimize choices.
• Clothing Rental: Preview the effect in advance to enhance the rental experience.
• Offline Experience: Create a virtual fitting area to attract customers.