Event Entry
What and Who
Structure-aware content creation – Detection, retargeting and deformation
Date, Time and Location
Abstract
visual representation is becoming indispensable to knowledge understanding and information
retrieval. Three-dimensional digitization plays a natural role in bridging connections between
the real and virtual world, which prompt the huge demand for massive three-dimensional
digital content. But reducing the effort required for three-dimensional modeling has been
a practical problem, and long standing challenge in compute graphics and related fields.
In this talk, we propose several techniques for lightening up the content creation process,
which have the common theme of being structure-aware, i.e. maintaining global relations
among the parts of shape. We are especially interested in formulating our algorithms such
that they make use of symmetry structures, because of their concise yet highly abstract
principles are universally applicable to most regular patterns.
We introduce our work from three different aspects in this thesis. First, we characterized
spaces of symmetry preserving deformations, and developed a method to explore this space
in real-time, which significantly simplified the generation of symmetry preserving shape
variants. Second, we empirically studied three-dimensional offset statistics, and developed
a fully automatic retargeting application, which is based on verified sparsity. Finally, we
made step forward in solving the approximate three-dimensional partial symmetry detection
problem, using a novel co-occurrence analysis method, which could serve as the foundation
to high-level applications.
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