Shape Synthesis Using Structure-Aware Reasoning

Abstract

Shape synthesis is an important area of computer vision and graphics that concerns

creation of new shapes and reconstruction from partial data. Its goal is to learn a

model that can generate shapes within an object category suitable for novel shape creation,

interpolation, completion, editing, and other geometric modeling applications.

Existing tools learn shape properties from large collections of shapes. Although these

methods have been very successful at learning how to synthesize the coarse shapes

of objects in categories with highly diverse shapes, they have not always produced

examples that reconstruct important structural elements of a shape. In this thesis,

I describe how structure can be incorporated into the synthesis process, and how it

can be used to improve generative models.

First, I introduce a template-dened skeleton structure for learning a part-aware

generative model in typography, where the shapes have a known structure and can

be explained by a small number of templates. Next, I present a scenario of noisy

archaeological wall painting (fresco) reconstruction from eroded fragments, where

there is no well-dened structure and exponentially many arrangement possibilities in

this case, I present a cluster evaluation function that guides the assembly process and

encourages selection of good clusters. Finally, I describe a semantic landmark-based

structure and how it can be used to improve a generative model of examples with

extremely varied topology by means of a geometric shape-structure consistency loss.

Through exploration of each type of structure, I show how reasoning with proposed

structures helps synthesize more accurate and realistic shapes. I also propose a fully

automatic framework for font completion. Finally, I design a genetic algorithm for

wall painting reconstruction and propose an iterative outlier detection technique based

on the eigenvector method.