levihsu/OOTDiffusion

Features

• Latent Diffusion Models - Based on a latent diffusion model that performs iterative denoising within a compressed latent space.
• Fashion Visualization - Facilitates digital fashion prototyping by creating visual previews of clothing on human bodies.
• Feature Fusion Architectures - Employs dual-path feature fusion to merge person and clothing representations for structural alignment and texture preservation.
• Cross-Attention Mechanisms - Utilizes cross-attention mechanisms to align garment visual features with specific spatial regions of the human figure.
• Image-Conditioned Generation - Implements generation guided by garment images and human poses as structural and stylistic references.
• Variational Autoencoders - Utilizes a variational autoencoder to compress high-resolution images into low-dimensional tensors.
• Iterative Denoising Pipelines - Uses a U-Net based architecture for the iterative denoising process essential to image synthesis.
• Human Image and Video Generation - Generates realistic images of human figures wearing specific garments for half-body and full-body compositions.
• Virtual Try-On Systems - Provides a deep learning system for mapping clothing items onto human images via virtual try-on.
• Image-to-Image Synthesis Frameworks - Implements a framework for merging garment and person images using reference guidance for precise overlays.
• Virtual Fitting Rooms - Enables virtual fitting by visualizing how specific garments look on human figures for e-commerce applications.

Star history