MULTI-STYLE TRANSFORMATION APPARATUS AND METHOD

Smart overview of the Invention

The multi-style transformation apparatus and method aim to enhance style transformation technology by employing a pre-trained deep neural network. This system facilitates the transformation of styles in specific regions of a content image. It includes a region segmentation unit for identifying areas to be transformed, a style transformation mask generation unit for creating multi-channel masks, and a multi-style transformation model that outputs the final transformed image.

Technical Background

Traditional methods for image style transformation rely on paired and non-paired datasets, often requiring multiple datasets for different styles. These methods face challenges in data collection and may not effectively handle diverse transformations like gender or age changes. Existing technologies, such as StyleGan2, offer high-quality transformations but are computationally intensive and apply styles uniformly across the entire image.

Proposed Solution

This invention addresses the limitations of current technologies by enabling high-quality transformations in specific image regions using a single neural network. The apparatus includes components for segmenting regions, generating multi-channel masks, and adjusting style intensities per region. This approach allows users to apply different styles to various parts of an image while maintaining overall quality and fidelity.

Methodology

The method involves segmenting regions in a content image, generating multi-channel masks for each region, and using a pre-trained neural network to produce the transformed image. Image data conversion techniques are employed to standardize input formats. Users can set style intensities manually or through predefined mappings, allowing for customizable transformations.

Training and Error Analysis

The training apparatus includes a database of indexed content-style image pairs and units for generating training data and analyzing transformation errors. The neural network is trained to minimize these errors, ensuring accurate style application across various regions. This process enhances the model's ability to perform complex transformations efficiently.