2026-09-20T02:12:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1229902026-06-06T00:48:46Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Pattie Maes.
Zhou, Lily,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:00:29Z
2019-11-22T00:00:29Z
2019
2019
https://hdl.handle.net/1721.1/122990
1127290502
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 55-58).
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.
by Lily Zhou.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
58 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Paper Dreams : an adaptive drawing canvas supported by machine learning
Adaptive drawing canvas supported by machine learning
Thesis
application/pdf
2019-11-22T00:00:24Z
Publication
Master
EECS
unknown
unknown
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