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SHMT
Overview :
SHMT is a self-supervised hierarchical makeup transfer technology achieved through latent diffusion models. This technology allows for the natural transfer of one facial makeup to another without the need for explicit labeling. Its main advantages include the ability to handle complex facial features and expression changes, providing high-quality transfer results. This technology has been accepted at NeurIPS 2024, showcasing its innovation and practicality in the field of image processing.
Target Users :
This product is suitable for researchers involved in facial makeup transfer, image processing engineers, and users interested in personalized makeup. It helps researchers explore new image processing techniques, provides efficient tools for engineers, and offers personalized makeup experiences for users.
Use Cases
Transfer a classic makeup look to a user-provided facial photo for personalized beauty recommendations
In film production, quickly transfer a specific character's makeup to an actor's face
In virtual try-on applications, provide users with real-time makeup previews and suggestions
Features
Self-supervised learning: Training without annotated data
Hierarchical transfer: Supports makeup transfer from basic to complex levels
High-quality output: Generated makeup appears natural and realistic
Multi-modal input support: Combines images, segmentation maps, and depth maps for transfer
Flexible model configuration: Supports varying levels of model configurations to adapt to different application scenarios
Available pre-trained models: Provides pre-trained models for quick transfer tasks
Easily extensible: Can be integrated with other image processing techniques
How to Use
Download and install the Python environment and necessary dependencies
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Clone the SHMT project code from GitHub
Download the pre-trained model and place it in the specified directory
Modify the parameters in the configuration file as needed
Run the transfer script, specifying the paths for the source and reference images
View the generated transfer results and perform subsequent processing
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