Dynamic sketching with structure-aware shape analysis

Abstract

Line drawings can be remarkably effective at conveying shape and meaning while reducing visual clutter. Inspired by the effectiveness and aesthetic appeal of human line drawings, researchers have developed 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 which requires only two-finger operations. We develop structure-aware shape analysis methods to obtain the intended drawing trajectories for organic objects and urban scenes separately, which address the question of what do people draw. Apart from the trajectories that depict visual features, we focus 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 view the sketching process as analogous to an information delivery process. A novel concept of sketching entropy, which measures the shape information of a stroke, is introduced. We obtain the complete sequence of a drawing by requiring every next drawn stroke to maximize 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 of Award	2015
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology