Paper Dreams : an adaptive drawing canvas supported by machine learning

Despite numerous recent advances in the field of deep learning for artistic purposes, the integration of these state-of-the-art machine learning tools into applications for drawing and visual expression has been an underexplored field. Bridging this gap has the potential to empower a large subset of the population, from children to the elderly, with a new medium to represent and visualize their ideas. Paper Dreams is a web-based canvas for sketching and storyboarding, with a multimodal user interface integrated with a variety of machine learning models. By using sketch recognition, style transfer, and natural language processing, the system can contextualize what the user is drawing; it then can color the sketch appropriately, suggest related objects for the user to draw, and allow the user to pull from a database of related images to add onto the canvas. Furthermore, the user can influence the output of the models via a serendipity dial that affects how "wacky" the system's outputs are. By processing a variety of multimodal inputs and automating artistic processes, Paper Dreams becomes an efficient tool for quickly generating vibrant and complex artistic scenes.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Zhou, Lily,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Pattie Maes.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122990

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122990