Evan Shimizu

Improving Parameterized Design with Interactive User-Guided Sampling and Parameter Identification Tools Degree Type: Ph.D. in Computer Science
Advisor(s): Kayvon Fatahalian, James McCann
Graduated: August 2020

Abstract:

Modern computer graphics design tasks often take place in high-dimensional parameterized design spaces. In these spaces, the design is specified by the value of tens to hundreds of parameters which often interact in ways that are difficult to predict. For instance, a parametric font may have tens of parameters controlling everything from stroke thickness to serif appearance, while a parameterized material may have hundreds of parameters specific to the material, such as a brick material providing controls for number of bricks per row and column, but no single brick size parameter. In these parameterized domains, creating a design often follows a coarse-to-fine iterative process where a designer creates a set of initial designs that are gradually refined until a design meeting all constraints is created. Per-parameter interfaces commonly used for parameterized design are not well-aligned with this process. One common method of providing higher-level navigation is to enable visual exploration by using a design gallery. This gallery presents an organized collection of samples selected from the design space that the user can browse through. Gallery interfaces provide a solid overview of a design space, but are difficult to direct to specific regions based on the user's current design goal.

This thesis presents a collection of software systems and interface techniques that support productive design in high-dimensional parameter spaces through interactive user-guided sampling. A major component of this work is Design Adjectives, a domain agnostic framework for creating parameterized design tools that use machine learned models of user intent to guide exploration through high-dimensional design spaces. The combination of a design gallery with a model of intent creates an interactive exploration interface that is more closely aligned with how the design process works. An implementation of the design adjectives system based on Gaussian process regression is presented. This implementation is able to rapidly learn user intent from only a few examples, and can generate samples of desireable designs for gallery viewing at interactive rates. Components of this framework can be improved on a domain-specific basis, and an example of such an improvement is examined in a theatrical lighting design context. Information gleaned from these exploratory design methods can be used in conjunction with parameter identification tools, such as the Hover Visualization tool presented in this thesis, to help support fine-tuning. In user studies evaluating these systems, users felt that they were able to explore the design space more quickly and easily when compared to existing per-parameter interface.

Thesis Committee:
Kayvon Fatahalian (Co-Chair)
James McCann (Co-Chair)
Brad Myers
Sylvain Paris (Adobe)

Srinivasan Seshan, Head, Computer Science Department
Martial Hebert, Dean, School of Computer Science

Keywords:
Creativity support tools, design tools, parameterized design, sampling methods, design interfaces, lighting design, image editing