Click here to download the project base paper on the taming of diffusion models.
Abstract:
Virtual try-on is a critical image synthesis task that aims to transfer clothes from one image to another while preserving the details of both humans and clothes. While many existing methods rely on Generative Adversarial Networks (GANs) to achieve this, flaws can still occur, particularly at high resolutions. Recently, the diffusion model has emerged as a promising alternative for generating high-quality images in various applications. However, taming simply using clothes as a condition for guiding the diffusion model to paint is insufficient to maintain the details of the clothes. To overcome this challenge, we propose an exemplar-based inpainting approach that leverages a warping module to guide the diffusion model’s generation effectively.
The warping module performs initial processing on the clothes, which helps to preserve the local details of the clothes. We then combine the warped clothes with a clothes-agnostic person image and add noise as the input of the diffusion model. Additionally, the warped clothes is used as local conditions for each denoising process to ensure that the resulting output retains as much detail as possible. Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON (DCI-VTON), effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results. Experimental results on VITON-HD demonstrate the effectiveness and superiority of our method.
Abstract:
This project aims to revolutionize the online shopping experience by introducing a Virtual Fashion Try On system. Leveraging Python and web technologies, the proposed system employs computer vision and augmented reality to allow users to virtually try on clothing items before making a purchase. The system enhances user engagement, reduces return rates, and provides a more personalized and immersive online shopping experience.
Existing System:
Current online shopping platforms often rely on static images and size charts for users to visualize how a clothing item might look on them. This approach lacks interactivity and personalization, leading to uncertainties and potential dissatisfaction with the purchased items.
Proposed System:
The proposed system introduces an advanced Virtual Fashion Try On system that utilizes computer vision and augmented reality. Users can upload their images, and the system dynamically superimposes virtual clothing items onto their digital representations. This enables users to visualize how different clothing items fit, look, and feel in real-time. The system’s web interface provides an interactive platform for users to explore and virtually try on various fashion items.
Modules Explanation:
- Image Processing Module:
- Processes user-uploaded images and extracts relevant features for virtual fitting.
- Clothing Item Rendering Module:
- Utilizes 3D rendering to superimpose virtual clothing items onto user images.
- Augmented Reality Integration:
- Integrates augmented reality techniques for realistic visualization and interaction.
- Web Interface:
- Provides an intuitive platform for users to upload images, browse clothing items, and virtually try on different outfits.
System Requirements:
- Hardware:
- Standard computer or mobile device with a camera.
- GPU for efficient image processing and rendering.
- Software:
- Python for implementing computer vision algorithms.
- Web development framework (e.g., Flask or Django).
- Augmented reality libraries (e.g., AR.js).
Algorithms:
- Computer Vision Algorithms:
- Utilizes image processing techniques for facial recognition, feature extraction, and virtual fitting.
Hardware and Software Requirements:
- Hardware:
- Standard computer or mobile device with a camera.
- GPU for efficient image processing and rendering.
- Software:
- Python 3.x
- Web development framework (Flask or Django).
- Augmented reality libraries (AR.js).
Architecture:
- Image Processing and Feature Extraction:
- Processes user-uploaded images and extracts relevant features for virtual fitting.
- Clothing Item Rendering:
- Utilizes 3D rendering techniques to superimpose virtual clothing items onto user images.
- Augmented Reality Integration:
- Integrates augmented reality for realistic visualization and interaction.
- Web Interface:
- User-friendly interface for uploading images, exploring clothing items, and virtually trying on outfits.
Technologies Used:
- Python, computer vision libraries for image processing.
- Web development frameworks (Flask/Django) for creating the web interface.
- Augmented reality libraries (AR.js) for realistic visualization.
Web User Interface:
The web interface offers a seamless and immersive experience for users to virtually try on clothing items. Users can upload their images, browse a catalog of virtual clothing items, and instantly visualize how different outfits look on them in real-time. The interface provides interactive controls for adjusting clothing items, exploring various styles, and making informed decisions before making a purchase. The Virtual Fashion Try On system enhances user engagement and brings a new dimension to online fashion shopping.
UML DIAGRAMS
Collaboration Diagram
Architecture diagram
class diagram
sequence diagram
use case diagram
activity diagram
component diagram
Deployment Diagram
Flow chart Diagram
