this article, we provide a comprehensive solution to the problem of transferring the observations of the motion capture sensors to an animated character whose size and proportion may be different from the performer's. Our goal is to map as many of the important aspects of the motion to the target character as possible, while meeting the online, real-time demands of computer puppetry. We adopt a Kalman filter scheme that addresses motion capture noise issues in this setting. We provide the notion of dynamic importance of an end-effector that allows us to determine what aspects of the performance must be kept in the resulting motion. We introduce a novel inverse kinematics solver that realizes these important aspects within tight real-time constraints. Our approach is demonstrated by its application to broadcast television performances

An efficient Inverse Kinematics solver is a key element in applications targeting the on-line or off-line postural control of complex articulated figures. In the present paper we progressively describe the strategic components of a very general and robust IK architecture. We then present an efficient recursive algorithm enforcing an arbitrary number of strict priorities to arbitrate the fulfillment of conflicting constraints. Due to its local nature, the moderate cost of the solution allows this architecture to run within an interactive environment. The algorithm is illustrated on the postural control of complex articulated figures.

While motion capture is commonplace in character animation, often the raw motion data itself is not used. Rather, it is first fit onto a skeleton and then edited to satisfy the particular demands of the animation. This process can introduce artifacts into the motion. One particularly distracting artifact is when the character's feet move when they ought to remain planted, a condition known as footskate. In this paper we present a simple, efficient algorithm for removing footskate. Our algorithm exactly satisfies footplant constraints without introducing disagreeable artifacts.

We describe a Parameterized Action Representation (PAR) designed to bridge the gap between natural language instructions and the virtual agents who are to carry them out. The PAR is therefore constructed based jointly on implemented motion capabilities of virtual human figures and linguistic requirements for instruction interpretation. We will

This paper presents a method to retarget the motion of a character to another in real-time. The technique is based on inverse rate control, which computes the changes in joint angles corresponding to the changes in end-effector position. While tracking the multiple end-effector trajectories of the original subject or character, our on-line motion retargetting also minimizes the joint angle differences by exploiting the kinematic redundancies of the animated model. This method can generalize a captured motion for another anthropometry to perform slightly different motion, while preserving the original motion characteristics. Because the above is done in on-line, a real-time performance can be mapped to other characters. Moreover, if the method is used interactively during motion capture session, the feedback of retargetted motion on the screen provides more chances to get satisfactory results. As a by-product, our algorithm can be used to reduce measurement errors in restoring captured motion. The data enhancement improves the accuracy in both joint angles and end-effector positions. Experiments prove that our retargetting algorithm preserves the high frequency details of the original motion quite accurately.

In this paper we provide methods that allow for path-based editing of existing motion data. We begin by exploring the concept of path as an abstraction of motion, and show how many of the common motion editing tools fail to provide proper control over this useful property. We provide a simple extension to displacement mapping methods that provide better control over the path in a manner that is easy to implement in current systems. We then extend this simple method to avoid violation of geometric constraints such as footskate. Keywords: Animation, Animation with Constraints, Interaction Techniques 1

This paper introduces Gesture Engine, an animation system that synthesizes human gesturing behaviors from augmented conversation transcripts using a database of highlevel gesture definitions. An abstract scripting language to specify hand-arm gestures is introduced that incorporates knowledge from sign language research, psycholinguistics, and traditional keyframe animation. A new planning algorithm instantiates and adjusts gestures according to communicative context and temporal constraints obtained from a speech synthesizer. The system animates an MPEG-4 compliant skeleton using Body Animation Parameters. 1

In this paper, we propose an example-based approach to on-line locomotion synthesis. Our approach consists of two parts: motion analysis and motion synthesis. In the motion analysis part, an unlabeled motion sequence is first decomposed into motion segments, exploiting the behavior of the COM (center of mass) trajectory of the performer. Those motion segments are subsequently classified into groups of motion segments such that the same group of motion segments share an identical footstep pattern. Finally, we construct a hierarchical motion transition graph by representing these groups and their connectivity to other groups as nodes and edges, respectively. The coarse level of this graph models locomotive motions and their transitions, and the fine level mainly captures the cyclic nature of locomotive motions. In the motion synthesis part, given a stream of motion specifications in an on-line manner, the motion transition graph is traversed while blending the motion segments to synthesize a motion at a node, one by one, guided by the motion specifications. Our main contributions are the motion labeling scheme and a new motion model, embodied by the hierarchical motion transition graph, which together enable not only artifact-free motion blending but also seamless motion transition.

Automatically animating virtual humans with actions that reflect real human motions is still a challenge. We present a framework for animation that is based on utilizing empirical and validated data from movement observation and cognitive psychology. To illustrate these, we demonstrate a mapping from Effort motion factors onto expressive arm movements, and from cognitive data to autonomous attention behaviors. We conclude with a discussion on the implications of this approach for the future of real-time virtual human animation. 1 Introduction Automatically animating virtual humans with actions that reflect real human motions is still a challenge. Skilled animators are able to create effective and compelling human characters through labor-intensive scripting of every subtlety of motion. For example, in producing the movie A Bug's Life, Pixar animators manipulate huge spreadsheets of animation parameters [41]. As we expect more human-like real-time behaviors, we cannot afford the tempor...

This article attempts to provide an overview of the process of creating animated motion from observations of real moving objects, and to discuss the potential for computer vision to contribute to this. My view is that the needs of the entire process create requirements on the individual steps; that motion capture for animation is most useful when the use of that data, including mapping and editing, is considered. The task of creating animation has some unique demands, and that only by considering these demands can a capture method be a useful tool for motion creation. This article is organized as follows. We begin with a discussion of the use of motion capture to create motion for animation, and look at the alternatives. We then consider the entire process of creating animation from motion capture, and consider some of these steps in detail. Specifically, we examine the current technologies for capture and issues in working with motion data. We conclude by discussing the opportunities for computer vision in the process. Within the animation community, there is historically a tension between animators and motion capture technicians/users [Cameron]. This tension comes from many factors, some of them real and some of them perceived. The two main sources of this tension are unrealistic expectations about what motion capture can do (that it can automatically produce motion that displaces animators), and that motion capture technology development has not considered the use of the data, leaving animators with data that is difficult to deal with. Motion Capture vs. Animation from Observation