Dynamic Sketching Using Structure-aware Shape Analysis

PhD Thesis Proposal Defence


Title: "Dynamic Sketching Using Structure-aware Shape Analysis"

by

Mr. Jingbo LIU


Abstract:

Line drawings can be remarkably efficient at conveying shape and meaning 
while reducing visual clutter. Inspired by the effectiveness and aesthetic 
appeal of human line drawings, researchers have investigated algorithms 
for generating line drawings from 3D meshes. Almost all such techniques 
focus on only the end product; very few regard the line drawings as a 
creative process. The creation process of a drawing provides a vivid 
visual progression, allowing the audience to better comprehend the 
drawing. It also enables numerous stroke-based rendering techniques.

In this dissertation, we address the problem of simulating the process of 
observational drawing; that is, what and how people draw when sketching. 
Given a 3D model and a viewpoint, our method synthesizes a visually 
plausible simulation of an observational sketching process. To 
conveniently change the view, we design a novel touch-based interface that 
supports six degrees of freedom 3D direct manipulation while requires only 
two-finger operations.

We develop structure-aware shape analysis methods to obtain the intended 
drawing trajectories, which address the question of what do people draw. 
Apart from the trajectories which depict visual features using 
conventional local geometric properties, we focus more on the auxiliary 
trajectories indicating the composition of the drawing. We extract 
auxiliary trajectories from contextual properties such as the topological 
layout, proportions of object parts, fitted primitives, partial 
symmetries, and levels of abstractions.

We develop the humanized stroke synthesis and stroke ordering methods to 
address the question of how do people draw. The stroke synthesis method 
simulates the action of a human moving a pen along an intended trajectory 
using a feedback control system. It produces human-like tentative strokes 
with inexact tracing and retracing effects. To assign a drawing order to 
the strokes, we approximate the sketching process with an information 
delivery process. A novel concept of the sketching entropy, which measures 
the shape information of a stroke, is introduced. We obtain the complete 
drawing order by requiring every next drawn stroke maximizes the 
information gain. Finally, we use the humanized strokes and their ordering 
to create the sketching animation.

We conduct a user study to evaluate the visual plausibility of the 
simulated drawing processes and the effectiveness of our proposed method. 
Experiment confirms that our results are visually plausible. The 
statistical analysis shows that our entropy-based ordering strategy leads 
to more plausible results than those driven by the conventional Gestalt 
rules used in previous works.


Date:			Friday, 22 May 2015

Time:                   10:00am - 12:00noon

Venue:                  Room 2132C
                         lift 19

Committee Members:	Prof. Chiew-Lan Tai (Supervisor)
  			Dr. Pedro Sander (Chairperson)
 			Prof. Albert Chung
  			Prof. Long Quan


**** ALL are Welcome ****