Identifying Virtual Episodes Based on Character Animation Traces
Keywords: Intelligent Agent, Animation Analysis, Storytelling, Interactive Narrative
Recognizing and captioning the occurrence of virtual episodes can add descriptive capabilities to games and simulations featuring human-like agents. We introduce a captioning heuristic for multi-character animations. The input of our algorithm consists of the traces (lowest-level procedure names) of each character's animation, such as walking, running, talking, reaching, etc. To identify virtual episodes from these traces we pre-authored episode-centric trees called Core Components Trees (CCT). We compute a vagueness measure over each possible match between episode CCTs and the given trace inputs using fuzzy logic, and derive the best match to describe and caption the perceived episodes.
Kuan Wang, Norman I. Badler. Identifying Virtual Episodes Based on Character Animation Traces. The 9th ACM SIGGRAPH International Conference on Motion in Games (MIG), in conjuction with AIIDE 2016, San Francisco, California, October 2016.
An Environment for Transforming Game Character Animations Based on Nationality and Profession Personality Stereotypes
Keywords: Human Simulation, NPC Creation, Personality, Motion Control, Authoring Tool
A vast body of literature has dealt with the challenges of creating the impression of human appearance and human-like motion in the animation of game characters. In this paper, we further refine these efforts by creating a flexible environment for animating game characters endowed with personality, which is a core descriptor of stable characteristics of human behavior and which is often expressed in human movement. We base our work on the Big Five personality traits, OCEAN. The environment we created incorporates a procedural mapping from OCEAN personality traits to movement modifiers that alter existing motions in ways compatible with a desired personality. Using Amazon Mechanical Turk, we collected stereotypical personality profiles for classes of characters, specifically 135 nationalities and 100 professions. We integrated these stereotypical personality expectations into an interactive interface in Unity3D. Users can linearly blend the nationality and profession OCEAN parameters, or even individually adjust them, for distinctive characters or groups. The results are validated using Amazon Mechanical Turk pairwise judgments on character types based on movements.
Funda Durupinar, Kuan Wang, Ani Nenkova and Norman I. Badler. An Environment for Transforming Game Character Animations Based on Nationality and Profession Personality Stereotypes. The 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), in conjunction with MIG 2016, San Francisco, California, October 2016.
Detecting Visually Observable Disease Symptoms from Faces
Keywords: Computer Vision, Clinical Analytics and Informatics, Machine Learning, Imbalanced Dataset
Recent years have witnessed an increasing interest in the application of machine learning to clinical informatics and healthcare systems. A significant amount of research has been done on healthcare systems based on supervised learning. In this study, we present a generalized solution to detect visually observable symptoms on faces using semi-supervised anomaly detection combined with machine vision algorithms. We rely on the disease-related statistical facts to detect abnormalities and classify them into multiple categories to narrow down the possible medical reasons of detecting. Our method is in contrast with most existing approaches, which are limited by the availability of labeled training data required for supervised learning, and therefore offers the major advantage of flagging any unusual and visually observable symptoms.
Kuan Wang, Jiebo Luo. Detecting Visually Observable Disease Symptoms from Faces. EURASIP Journal on Bioinformatics and Systems Biology, 2016(1), 1-8. DOI= 10.1186/s13637-016-0048-7.
Kuan Wang, Jiebo Luo. Images for multiple symptoms, University of Rochester. Available from: http://tinyurl.com/h77ty86.
Kuan Wang, Jiebo Luo. Detecting Visually Observable Disease Symptoms from Faces. The 1st International Workshop on Biomedical Informatics with Optimization and Machine Learning (BOOM), in conjuction with IJCAI 2016, New York City, New York, July 2016.
Best Paper Runner-up Award