First Principles of Line Drawings

This thesis presents an unsupervised method for creating line drawings from photographs or 3D models. Current methods often rely on high quality paired datasets to automate the creation of line drawings. We observe that line drawings are encodings of scene information that convey 3D shape and semantic meaning. We bake these observations into a set of first principle objectives and train an image translation network to map 3D objects into line drawings. We also explore generation of new styles of line drawings through a novel style confusion loss which averages and combines elements from different styles in a structured manner. User studies and quantitative experiments validate that our method encodes geometry and semantic information into line drawings and improves overall drawing quality.

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

2021

Author dc:creator

• Chan, Caroline

Advisor dc:contributor.advisor

• Durand, Frédo

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139322

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139322