Semantics-Guided Exploration of Latent Spaces for Shape Synthesis

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Creator: 

Jahan, Tansin

Date: 

2020

Abstract: 

We introduce an approach to incorporate user guidance into shape synthesis approaches based on deep networks. Our main idea is to allow users to start an exploratory process of the shape space with the use of high-level semantic keywords. Specifically, the user inputs a set of keywords that describe the general attributes of the shape to be generated. Next, we map the keywords to a subspace of the latent space that captures the shapes possessing the specified attributes. The user then explores only the subspace to search for shapes that satisfy the design goal. Our technical contribution is the introduction of a label regression neural network coupled with a shape synthesis neural network. The label regression network takes the user-provided keywords and maps them to the corresponding subspace of the latent space, where the subspace is modeled as a set of distributions for the dimensions of the latent space.

Subject: 

Computer Science

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Computer Science: 
M.C.S.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

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