A copy of her thesis is available upon request.
Everyone is invited to attend her talk. The talk abstract follows below.
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-defined 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-defined 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.