GarDiff - AI virtual try-on technology that generates high-fidelity try-on images while preserving clothing details.
GarDiff is an innovative virtual try-on technology that uses CLIP and VAE encoding to extract the appearance prior of clothing, combined with a clothing focus adapter and high-frequency detail enhancement algorithms, to generate high-fidelity and richly detailed try-on images.
What is GarDiff?
GarDiff is an innovative virtual try-on technology that extracts the appearance prior of clothing using CLIP and VAE encoding. Combined with a clothing focus adapter and high-frequency detail enhancement algorithms, it generates high-fidelity and richly detailed try-on images. It accurately aligns clothing with human poses, preserving complex patterns and textures, and providing a realistic online try-on experience. GarDiff outperforms existing technologies on the VITON-HD and DressCode datasets. Its code is open-source and available for further research and application development.
GarDiff's main functions
- High-fidelity try-on image generation:GarDiff can generate high-resolution and realistic try-on images, preserving the features of the subject and the details of the clothing.
- Clothing details preserved:We focus on preserving every detail of the garment's appearance and texture, including intricate patterns and text.
- Clothing aligned with human posture:GarDiff uses special adapters to ensure that clothing visually aligns perfectly with the human body's posture, regardless of how the posture changes.
- Clothing Focus Diffusion Process:GarDiff employs a clothing-centric diffusion process, paying particular attention to the details of clothing when generating images.
- Prior guidance based on appearance:The appearance priors of the reference garment are extracted using CLIP and VAE encoding, and this prior information serves as an additional condition for guiding image generation.
GarDiff's technical principles
- CLIP and VAE encodingThe appearance priors of the reference garment are extracted based on the CLIP visual encoder and VAE encoder. These priors serve as additional conditions to guide the diffusion process.
- Clothing Focusing Adapter (GF Adapter)In each Transformer block, the original cross-attention layer is replaced with the proposed garment-focused visual adapter module. The adapter handles CLIP visual embedding and VAE embedding separately through a decoupled cross-attention mechanism.
- Appearance LossA novel appearance loss is proposed, which includes Spatial Perceptual Loss and High-Frequency Promoted Loss, to enhance the model’s ability to generate high-frequency details.
GarDiff's project address
- Github repository:https://github.com/siqi0905/GarDiff/tree/master
- arXiv technical paper:https://arxiv.org/pdf/2409.08258v1
Application scenarios of GarDiff
- e-commerce:Online retailers can use GarDiff to offer virtual try-on functionality, allowing customers to see how clothing would look on them before purchasing, thus improving the shopping experience and satisfaction.
- Fashion Design:Fashion designers can use GarDiff to preview how design sketches look on different models, allowing for rapid iteration and adjustments to the design.
- Personalized recommendations:E-commerce platforms can use GarDiff to generate personalized try-on effects based on users' body shape and preferences, and recommend more suitable clothing to users.
- social media:Users can share their virtual try-on photos on social media, increasing interactivity and entertainment.
- Virtual Fashion Show:Fashion brands can use GarDiff to create virtual fashion shows and showcase their latest collections without the need for physical clothing or live models.
- Games and Virtual Reality:In virtual worlds or games, players can use GarDiff to customize and preview their virtual avatars, enhancing immersion.