This MRP will interrogate and discuss the possibility of applying machine learning technology into storyboarding processes from an animation and film industry perspective. In current animation or film production studios, most of a storyboarder's creative process is a repeating process of manually editing and visualizing content from a script. It is a storyboarders’ responsibility to analyze camera cuts and scenes information, then organize the scenes, the director's notes, and camera movements in storyboard drawing software to create a working template. The storyboarder is always responsible for hundreds of such repeating steps in storyboarding processes. Those repeating processes are all inefficient and could be limiting. This paper will analyze and review many machine learning technology methods in the current animation and film industry. The literature review part will identify many machine learning methods’ positions in the animation production pipeline and their advantages and disadvantages in related professional fields. Finally, based on all the wide-ranging literature reviews and unique perspectives towards applying machine learning into storyboarding and storytelling. This research paper will address a theoretical concept of how to apply visual recognition, text mining and automation technology into storyboarding software to analyze the scripts for storyboarders.
Mingyue Yin is an enthusiastic animator and designer who graduated with a Bachelor of Animation Degree at Sheridan College. He is passionate about modern digital media, concept design and media production. An effective communicator and researcher with proven dedication in media production. Aiming to combine original specializations in animation and film with elegant digital media innovation to create advanced concepts in the modern media industry.