Bill Yuan-chi Chiu

Dynamic response for motion capture animation

Human motion capture embeds rich detail and style which is difficult to generate with competing animation synthesis technologies. However, such recorded data requires principled means for creating responses in unpredicted situations, for example reactions immediately following impact. This paper introduces a novel technique for incorporating unexpected impacts into a motion capture-driven animation system through the combination of a physical simulation which responds to contact forces and a specialized search routine which determines the best plausible re-entry into motion library playback following the impact. Using an actuated dynamic model, our system generates a physics-based response while connecting motion capture segments. Our method allows characters to respond to unexpected changes in the environment based on the specific dynamic effects of a given contact while also taking advantage of the realistic movement made available through motion capture. We show the results of our system under various conditions and with varying responses using martial arts motion capture as a testbed.

Detecting time series motifs under uniform scaling

Time series motifs are approximately repeated patterns foundwithin the data. Such motifs have utility for many data mining algorithms, including rule-discovery,novelty-detection, summarization and clustering. Since the formalization of the problem and the introduction of efficient linear time algorithms, motif discovery has been successfully applied tomany domains, including medicine, motion capture, robotics and meteorology. In this work we show that most previous applications of time series motifs have been severely limited by the definitions brittleness to even slight changes of uniform scaling, the speed at which the patterns develop. We introduce a new algorithm that allows discovery of time series motifs with invariance to uniform scaling, and show that it produces objectively superior results in several important domains. Apart from being more general than all other motifdiscovery algorithms, a further contribution of our work isthat it is simpler than previous approaches, in particular we have drastically reduced the number of parameters that need to be specified.

Breathe easy: model and control of human respiration for computer animation

In this paper, we detail an anatomically inspired, physically based model of the human torso designed for the visual simulation of respiration using a mixed system of rigid and deformable parts. Motion related to breath is a signature movement of the human body and an indicator for life but it has been largely overlooked by the graphics community. A novel composition of biological components is necessary to capture the key characteristics of breathing motion visible in the human trunk because the movement is generated fundamentally through the combination of both rigid bone and soft tissue. Our approach uses a simple physically based muscle element which is used throughout to drive the motion of the ribs and diaphragm as well as in other muscles, like those of the abdomen, to produce passive resistance. In addition, we describe an implementation of a straightforward method for preserving incompressible volume in deformable bodies to use in approximating the motion of the abdomen related to breath. Through the careful construction of this anatomically based torso, control for respiration becomes the generation of periodic contraction signals for a minimal set of two muscle groups. We show the flexibility of our approach through the animation of several breathing styles using our system.

Efficient discovery of unusual patterns in time series

The problem of finding a specified pattern in a time series database (i.e., query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of “surprise”, and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these limitations we propose a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data. This notion has the advantage of not requiring the user to explicitly define what is a surprising pattern, which may be hard, or perhaps impossible, to elicit from a domain expert. Instead, the user gives the algorithm a collection of previously observed “normal” data. Our algorithm uses a suffix tree to efficiently encode the frequency of all observed patterns and allows a Markov model to predict the expected frequency of previously unobserved patterns. Once the suffix tree has been constructed, a measure of surprise for all the patterns in a new database can be determined in time and space linear in the size of the database. We demonstrate the utility of our approach with an extensive experimental evaluation.

Model and control of simulated respiration for animation

Animation of the breath has been largely ignored by the graphics community, even though it is a signature movement of the human body and an indicator for lifelike motion. In this paper, we present an anatomically inspired, physically based model of the human torso for the visual simulation of respiration using a mixed system of rigid and deformable parts. This novel composition of anatomical components is necessary to capture the key characteristics of breathing motion visible in the human trunk because the movement is generated fundamentally through the combination of both rigid bone and soft tissue. We propose a simple anatomically meaningful muscle element based on springs, which is used throughout both actively to drive the motion of the ribs and diaphragm and passively for other muscles like those of the abdomen. In addition, we introduce a straightforward method for preserving incompressible volume in deformable bodies to use in approximating the motion of the gut related to breath. Through the careful construction of this anatomically based torso, control for respiration becomes the generation of periodic contraction signals for a minimal set of two muscle groups. We show the flexibility of our approach through the animation of several breathing styles using our system and we verify our results through video and analytical comparisons.