CONTENT AWARE BACKGROUND GENERATION

Smart overview of the Invention

Designers often face challenges in finding suitable backgrounds for digital images, which can be time-consuming and prone to errors. The background generation system addresses these issues by automating the process using a machine-learning model and generative artificial intelligence. This system forms a mask from a digital image to identify foreground and background regions, allowing it to generate a complementary background that enhances the overall design.

User Control and Flexibility

The system allows users to specify parameters that guide the background generation process, offering greater control over the final output. Users can adjust these parameters through a user interface, which may include controls like sliders for setting variance or selecting seed primary colors. This flexibility enables the creation of multicolored abstract backgrounds that adhere to a desired color theme, enhancing visual richness and coherence.

Machine-Learning Model Utilization

A compositional pattern producing neural network (CPPN) is employed to generate backgrounds based on the digital image, mask, and user-defined parameters. The model uses a loss function with various terms to guide the generation process, ensuring the background's opacity and color theme align with the user's specifications. This approach improves computational efficiency and accuracy over traditional methods.

Dynamic Background Adjustment

The system is capable of dynamically adjusting the background in response to changes in the foreground, a feature not typically available in conventional techniques. This adaptability ensures that the background remains harmonious with the foreground objects, maintaining the visual appeal of the digital image even as elements are modified.

Technical Advancements

By leveraging advanced machine-learning techniques, the background generation system offers significant improvements over traditional methods. It reduces computational resource consumption while increasing visual richness and user control. These advancements address the limitations of conventional background generation, providing a more efficient and effective solution for digital content design.