Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Real-time control of three-dimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this set of behaviors, possibly with a low-dimensional input device. One appealing approach to obtaining a rich set of avatar behaviors is to collect an extended, unlabeled sequence of motion data appropriate to the application. In this paper, we show that such a motion database can be preprocessed for flexibility in behavior and efficient search and exploited for real-time avatar control. Flexibility is created by identifying plausible transitions between motion segments, and efficient search through the resulting graph structure is obtained through clustering. Three interface techniques are demonstrated for controlling avatar motion using this data structure: the user selects from a set of available choices, sketches a path through an environment, or acts out a desired motion in front of a video camera. We demonstrate the flexibility of the approach through four different applications and compare the avatar motion to directly recorded human motion.

Interactive control of avatars animated with human motion data

Biological motion contains information about the identity of an agent as well as about his or her actions, intentions, and emotions. The human visual system is highly sensitive to biological motion and capable of extracting socially relevant information from it. Here we investigate the question of how such information is encoded in biological motion patterns and how such information can be retrieved. A framework is developed that transforms biological motion into a representation allowing for analysis using linear methods from statistics and pattern recognition. Using gender classification as an example, simple classifiers are constructed and compared to psychophysical data from human observers. The analysis reveals that the dynamic part of the motion contains more information about gender than motion-mediated structural cues. The proposed framework can be used not only for analysis of biological motion but also to synthesize new motion patterns. A simple motion modeler is presented that can be used to visualize and exaggerate the differences in male and female walking patterns.

/pdf/decomposing-biological-motion-a-framework-for-analysis-and-2czn00nmpf.pdf

Decomposing biological motion: A framework for analysis and synthesis of human gait patterns

The focus of motion analysis has been on estimating a flow vector for every pixel by matching intensities. In my thesis, I will explore motion representations beyond the pixel level and new applications to which these representations lead. 
I first focus on analyzing motion from video sequences. Traditional motion analysis suffers from the inappropriate modeling of the grouping relationship of pixels and from a lack of ground-truth data. Using layers as the interface for humans to interact with videos, we build a human-assisted motion annotation system to obtain ground-truth motion, missing in the literature, for natural video sequences. Furthermore, we show that with the layer representation, we can detect and magnify small motions to make them visible to human eyes. Then we move to a contour presentation to analyze the motion for textureless objects under occlusion. We demonstrate that simultaneous boundary grouping and motion analysis can solve challenging data, where the traditional pixel-wise motion analysis fails. 
In the second part of my thesis, I will show the benefits of matching local image structures instead of intensity values. We propose SIFT flow that establishes dense, semantically meaningful correspondence between two images across scenes by matching pixel-wise SIFT features. Using SIFT flow, we develop a new framework for image parsing by transferring the metadata information, such as annotation, motion and depth, from the images in a large database to an unknown query image. We demonstrate this framework using new applications such as predicting motion from a single image and motion synthesis via object transfer. Based on SIFT flow, we introduce a nonparametric scene parsing system using label transfer, with very promising experimental results suggesting that our system outperforms state-of-the-art techniques based on training classifiers. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Beyond pixels: exploring new representations and applications for motion analysis

straints, motion capture. In this paper, we present a technique for motion: the problem of adapting an animated motion from one character to another. Our focus is on adapting the motion of one articulated figure to another figure with identical structure but different segment lengths, although we use this as a step when considering less similar characters. Our method creates adaptations that preserve desirable qualities of the original motion. We identify specific features of the motion as constraints that must be maintained. A spacetime constraints solver computes an adapted motion that re-establishes these constraints while preserving the frequency characteristics of the original signal. We demonstrate our approach on motion capture data.

/pdf/retargetting-motion-to-new-characters-4t7e9e1rxt.pdf

Retargetting motion to new characters

This paper describes the method for modeling human figure locomotions with emotions. Fourier expansions of experimental data of actual human behaviors serve as a basis from which the method can interpolate or extrapolate the human locomotions. This means, for instance, that transition from a walk to a run is smoothly and realistically performed by the method. Moreover an individual's character or mood, appearing during the human behaviors, is also extracted by the method. For example, the method gets "briskness" from the experimental data for a "normal" walk and a "brisk" walk. Then the "brisk" run is generated by the method, using another Fourier expansion of the measured data of running. The superposition of these human behaviors is shown as an efficient technique for generating rich variations of human locomotions. In addition, step-length, speed, and hip position during the locomotions are also modeled, and then interactively controlled to get a desired animation. Abstract

/pdf/fourier-principles-for-emotion-based-human-figure-animation-30cxlv1oj8.pdf

Fourier principles for emotion-based human figure animation

A bending angle of each joint of a multiple-jointed object is represented with a function expressed independently of a length between joints of the object. Based on the function, contour data is produced for a motion of each joint, which is then displayed on a screen. With this provision, it is unnecessary to generate again functions each time the size of the object changes. Namely, the functions are independent of the size of the object. Since parameters of the functions can be altered to add characteristics to changes in the bending angles the respective joints, the object is actuated in an action having an emotional expression.

Method and apparatus for representing motion of multiple-jointed object, computer graphic apparatus, and robot controller

A biological image formation aiding system and a biological image forming method are provided in which three-dimensional data corresponding to a standard shape of a biological article, standard motion thereof, and a standard material feeling of an outer surface of the biological article are prepared, and a realistic three-dimensional synthesized image of an entire biological article with individuality can be formed simply. The system includes a shape data storage for storing data corresponding to a three-dimensional shape of a biological image; a motion data storage for storing data corresponding to three-dimensional motion of the biological image, a material feeling data storage for storing data corresponding to a three-dimensional material feeling of an outer surface of the biological image; editing/processing units capable of modifying at least one of the data corresponding to the three-dimensional shape of the biological image, the three-dimensional data corresponding to the motion of the biological image, and the data corresponding to the three-dimensional material feeling of the outer surface of the biological image in accordance with a producer's intent; and an output unit responsive to the outputs of the editing/processing unit for synthesizing the data corresponding to the three-dimensional biological image, the data corresponding to the three-dimensional motion of the biological image, and the data corresponding to the three-dimensional material feeling of the outer surface of the biological image after modification with each other to provide synthesized three-dimensional data for a biological image to be produced.

Biological image formation aiding system and biological image forming method

Hair models of human or, animal hairs are displayed on a display screen using a three-dimensional computer graphics technique. Each human hair model is constructed of a plurality of rod-shaped hair elements. The magnitude and direction of an external force applied to each hair element are designated. A deformation quantity of each hair element is obtained such that the external force having the designated magnitude and direction equilibrates with an internal force generated by the rigidity of each hair element. The shape of each hair model is determined in accordance with obtained deformation quantities, for display of the shape of each hair model. The hair model can be displayed man-machine interactively.

Method of displaying hair style and apparatus for the same

This paper describes a new functional generation method for human motion with emotion. In the method, the empirical data of the human joint angles in some typical motions with emotions are utilized. Based on the frequency analysis, the data are then classified into two groups, the basic factor and the additional factor. The former factor corresponds to a basic motion like “walking”, whereas the latter means a descriptive part of the basic motion like “brisk” or “fast”. Then, apart from the analysed emprical motion, more complex human motions with emotions are generated by interpolation and/or extrapolation of these factors in the frequency-phase domain. The sequences of the short animations obtained will illustrate the power of the method.

Generation of Human Motion with Emotion

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