Animal Face Style Generation With Keypoint Alignment

The patent segments the image transformation process into multiple specialized models: a face recognition model for detecting human faces, an animal face style generation model for transforming faces into animal styles, and an image fusion model for combining results. This segmentation allows each model to specialize in one aspect, enabling diverse style transformations while maintaining manageable system complexity through modular architecture.

The patent creates a universal transformation framework that can handle multiple animal face styles (cat, dog, rabbit, etc.) through a single multi-functional system. The animal face style generation model is designed to work with various animal types by learning from diverse training pairs, making the system adaptable to different style transformation requirements without needing separate specialized models for each animal type.

The patent applies preliminary action by pre-training the animal face style generation model with carefully constructed training pairs before deployment. The training process involves preparing paired images of human faces and corresponding animal faces in advance, and the model is pre-trained on these pairs to learn the transformation mappings. This preliminary training ensures high transformation accuracy when the model is used, while the complex training work is completed beforehand rather than during runtime.

The patent uses copying by creating training pairs where animal face images are generated or selected to correspond with human face images. These training pairs serve as templates that the model learns from, copying the structural and feature relationships between human and animal faces. This copying approach allows the model to achieve high accuracy by learning from representative examples without needing to understand the underlying biological complexities.