Machine Learning for Artistic Transformation
Description: This project utilizes machine learning to apply the aesthetic style of one image onto another while retaining the original content. The technique opens up possibilities for dynamic visual changes in games, where environments or characters could transform based on player interaction or narrative progression.
Relevance to Game Design: Style Transfer introduces a method for creating visually adaptive game worlds, blending artistic styles in real-time to match player experiences. This aligns with experimental game design and focuses on integrating art and technology.
All the photographs in this presentation are provided by Professor Legrady in Media Arts and Technology at UCSB.