Ronald A. Metoyer

Reactive pedestrian path following from examples

Architectural and urban planning applications require animations of people to present an accurate and compelling view of a new environment. Ideally, these animations would be easy for a non-programmer to construct, just as buildings and streets can be modeled by an architect or artist using commercial modeling software. In this paper, we explore an approach for generating reactive path following based on the user’s examples of the desired behavior. The examples are used to build a model of the desired reactive behavior. The model is combined with reactive control methods to produce natural 2D pedestrian trajectories. The system then automatically generates 3D pedestrian locomotion using a motion-graph approach. We discuss the accuracy of the learned model of pedestrian motion and show that simple direction primitives can be recorded and used to build natural, reactive, path-following behaviors.

Visualization of Diversity in Large Multivariate Data Sets

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.

Using cognitive dimensions: Advice from the trenches

Abstract Many researchers have analyzed visual language design using Cognitive Dimensions (CDs), but some have reinterpreted the purpose, vocabulary, and use of CDs, potentially creating confusion. In particular, those who have used CDs to convince themselves or others that their language is usable have tended to ignore or downplay the tradeoffs inherent in design, resulting in evaluations that provide few insights. Researchers who do not consider who , when , and how best to analyze a visual language using CDs are likely to miss the most useful opportunities to uncover problems in their visual languages. In this paper, we consider common breakdowns when using CDs in analysis. Then, using three case studies, we demonstrate how the who , when , and how circumstances under which CDs are applied impact the gains that can be expected.

Explaining how to play real-time strategy games

Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior.

Psychologically Inspired Anticipation and Dynamic Response for Impacts to the Head and Upper Body

We present a psychology-inspired approach for generating a characters anticipation of and response to an impending head or upper body impact. Protective anticipatory movement is built upon several actions that have been identified in the psychology literature as response mechanisms in monkeys and in humans. These actions are parameterized by a model of the approaching object (the threat) and are defined as procedural rules. We present a hybrid forward and inverse kinematic blending technique to guide the character to the pose that results from these rules while maintaining properties of a balanced posture and characteristics of the behavior just prior to the interaction. In our case, these characteristics are determined by a motion capture sequence. We combine our anticipation model with a physically-based dynamic response to produce animations where a character anticipates an impact before collision and reacts to the contact, physically, after the collision. We present a variety of examples including threats that vary in approach direction, size, and speed.

Anticipation from example

Automatically generated anticipation is a largely overlooked component of response in character motion for computer animation. We present an approach for generating anticipation to unexpected interactions with examples taken from human motion capture data. Our system generates animation by quickly selecting an anticipatory action using a Support Vector Machine (SVM) which is trained offline to distinguish the characteristics of a given scenario according to a metric that assesses predicted damage and energy expenditure for the character. We show our results for a character that can anticipate by blocking or dodging a threat coming from a variety of locations and targeting any part of the body, from head to toe.

Understanding the verbal language and structure of end-user descriptions of data visualizations

Tools exist for people to create visualizations with their data; however, they are often designed for programmers or they restrict less technical people to pre-defined templates. This can make creating novel, custom visualizations difficult for the average person. For example, existing tools typically do not support syntax or interaction techniques that are natural to end users. To explore how to support a more natural production of data visualizations by end users, we conducted an exploratory study to illuminate the structure and content of the language employed by end users when describing data visualizations. We present our findings from the study and discuss their design implications for future visualization languages and toolkits.

A data-driven model of pedestrian movement

We present a method for simulating individual pedestrian motion based on empirical data. Our model keeps track of the pedestrian’s position and body configuration (pose) and uses motion capture data to produce plausible motion. While our ultimate goal is creating 3D animations of crowds, our initial efforts focus on 2D simulations. In this paper, we present a 2D model for an able-bodied male. Using our approach, we could also capture data and build models for a heterogeneous population, including children, the elderly, pedestrians in wheelchairs, and people on crutches. We demonstrate the realism of our model with a small-scale test case and a larger crowd evacuation simulation.

A syntax-directed keyboard extension for writing source code on touchscreen devices

As touchscreen mobile devices grow in popularity, it is inevitable that software developers will eventually want to write code on them. However, writing code on a soft (or virtual) keyboard is cumbersome due to the device size and lack of tactile feedback. We present a soft syntax-directed keyboard extension to the QWERTY keyboard for Java program input on touchscreen devices and evaluate this keyboard with Java programmers. Our results indicate that a programmer using the keyboard extension can input a Java program with fewer errors and using fewer keystrokes per character than when using a standard soft keyboard alone. In addition, programmers maintain an overall typing speed in words per minute that is equivalent to that on the standard soft keyboard alone. The keyboard extension was shown to be mentally, physically, and temporally less demanding than the standard soft keyboard alone when inputting a Java program.

Interactive visual analysis promotes exploration of long-term ecological data

Long-term ecological data are crucial in helping ecologists understand ecosystem function and environmental change. Nevertheless, these kinds of data sets are difficult to analyze because they are usually large, multivariate, and spatiotemporal. Although existing analysis tools such as statistical methods and spreadsheet software permit rigorous tests of pre-conceived hypotheses and static charts for simple data exploration, they have limited capacity to provide an overview of the data and to enable ecologists to explore data iteratively, and interactively, before committing to statistical analysis. These issues hinder how ecologists gain knowledge and generate hypotheses from long-term data. We present Ecological Distributions and Trends Explorer (EcoDATE), a web-based, visual-analysis tool that facilitates exploratory analysis of long-term ecological data (i.e., generating hypotheses as opposed to confirming hypotheses). The tool, which is publicly available online, was created and refined through a user-centered design process in which our team of ecologists and visualization researchers collaborated closely. The results of our collaboration were (1) a set of visual representation and interaction techniques well suited to communicating distribution patterns and temporal trends in ecological data sets, and (2) an understanding of processes ecologists use to explore data and generate and test hypotheses. We present three case studies to demonstrate the utility of EcoDATE and the exploratory analysis processes using long-term data on cone production, stream chemistry, and forest structure collected as part of the H.J. Andrews Experimental Forest (HJA), Long Term Ecological Research (LTER), and US Forest Service Pacific Northwest Research Station programs. We also present results from a survey of 15 participants of a working group at the 2012 LTER All Scientists Meeting that showed that users appreciated the tool for its ease of use, holistic access to large data sets, and interactivity.