Performance-based control interfaces using mixture of factor analyzers.
ABSTRACT This paper introduces an approach to performance animation that employs a small number of inertial measurement sensors to
create an easy-to-use system for an interactive control of a full-body human character. Our key idea is to construct a global
model from a prerecorded motion database and utilize them to construct full-body human motion in a maximum a posteriori framework
(MAP). We have demonstrated the effectiveness of our system by controlling a variety of human actions, such as boxing, golf
swinging, and table tennis, in real time. One unique property of our system is its ability to learn priors from a large and
heterogeneous motion capture database and use them to generate a wide range of natural poses, a capacity that has not been
demonstrated in previous data-driven character posing systems.
- SourceAvailable from: psu.edu[show abstract] [hide abstract]
ABSTRACT: In this paper, we describe a novel technique, called motion texture, for synthesizing complex human-figure motion (e.g., dancing) that is statistically similar to the original motion captured data. We de- fine motion texture as a set of motion textons and their distribution, which characterize the stochastic and dynamic nature of the captured motion. Specifically, a motion texton is modeled by a linear dynamic system (LDS) while the texton distribution is represented by a transition matrix indicating how likely each texton is switched to another. We have designed a maximum likelihood algorithm to learn the motion textons and their relationship from the captured dance motion. The learnt motion texture can then be used to generate new animations automatically and/or edit animation sequences interactively. Most interestingly, motion texture can be manipulated at different levels, either by changing the fine details of a specific motion at the texton level or by designing a new choreography at the distribution level. Our approach is demonstrated by many synthesized sequences of visually compelling dance motion.08/2002;
Conference Proceeding: Parametric motion graphs.[show abstract] [hide abstract]
ABSTRACT: In this paper, we present an example-based motion synthesis tech- nique that generates continuous streams of high-fidelity, control- lable motion for interactive applications, such as video games. Our method uses a new data structure called a parametric motion graph to describe valid ways of generating linear blend transitions be- tween motion clips dynamically generated through parametric syn- thesis in realtime. Our system specifically uses blending-based parametric synthesis to accurately generate any motion clip from an entire space of motions by blending together examples from that space. The key to our technique is using sampling methods to iden- tify and represent good transitions between these spaces of motion parameterized by a continuously valued parameter. This approach allows parametric motion graphs to be constructed with little user effort. Because parametric motion graphs organize all motions of a particular type, such as reaching to different locations on a shelf, us- ing a single, parameterized graph node, they are highly structured, facilitating fast decision-making for interactive character control. We have successfully created interactive characters that perform se- quences of requested actions, such as cartwheeling or punching. CR Categories: I.3.7 (Computer Graphics): Three-Dimensional Graphics and Realism—AnimationProceedings of the 2007 Symposium on Interactive 3D Graphics, SI3D 2007, April 30 - May 2, 2007, Seattle, Washington, USA; 01/2007
Article: Style-based inverse kinematics.[show abstract] [hide abstract]
ABSTRACT: This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.ACM Trans. Graph. 01/2004; 23:522-531.