Figure 1: Examples from our test set of motions. The left two images are natural (motion capture data). The two images to the right are unnatural (badly edited and incompletely cleaned motion). Joints that are marked in red-yellow were detected as having unnatural motion. Frames for these images were selected by the method presented in [Assa et al. 2005]. In this paper, we investigate whether it is possible to develop a measure that quantifies the naturalness of human motion (as defined by a large database). Such a measure might prove useful in verifying that a motion editing operation had not destroyed the naturalness of a motion capture clip or that a synthetic motion transition was within the space of those seen in natural human motion. We explore the performance of mixture of Gaussians (MoG), hidden Markov models (HMM), and switching linear dynamic systems (SLDS) on this problem. We use each of these statistical models alone and as part of an ensemble of smaller statistical models. We also implement a Naive Bayes (NB) model for a baseline comparison. We test these techniques on motion capture data held out from a database, keyframed motions, edited motions, motions with noise added, and synthetic motion transitions. We present the results as receiver operating characteristic (ROC) curves and compare the results to the judgments made by subjects in a user study.

Figure 1: Animations synthesized by our motion interpolation in a 5D parametric space. One parameter changes the style of motion from rough to delicate as shown by the bar indicator. The other four parameters are the heights and widths of two successive steps of stairs for gait motions, and the 2D start and end locations of the box for lifting motions. None of the motions required post-cleaning of foot- or hand-sliding. A common motion interpolation technique for realistic human animation is to blend similar motion samples with weighting functions whose parameters are embedded in an abstract space. Existing methods, however, are insensitive to statistical properties, such as correlations between motions. In addition, they lack the capability to quantitatively evaluate the reliability of synthesized motions. This paper proposes a method that treats motion interpolations as statistical predictions of missing data in an arbitrarily definable parametric space. A practical technique of geostatistics, called universal kriging, is then introduced for statistically estimating the correlations between the dissimilarity of motions and the distance

Figure 1: Motion editing can produce significant footskate (Section 1). On the left is an edited motion capture sequence. We superimpose partially translucent renderings of frames spaced evenly in time. As a result, a slowly moving part of the body – like the skating foot plant in this image – shows up as a dark region with blurry outlines. We introduce a robust oracle for detecting foot plants. When coupled with an off-the-shelf footskate remover, our system behaves like a black box (center) that cleans up motion at interactive rates (right). The foot is now planted firmly, as one can see from the sharp outline around the toe. Notice that the mild blur at the heel on the right results from the way the heel and then the toes are planted. Footskate, where a character’s foot slides on the ground when it should be planted firmly, is a common artifact resulting from almost any attempt to modify motion capture data. We describe an online method for fixing footskate that requires no manual clean-up. An important part of fixing footskate is determining when the feet should be planted. We introduce an oracle that can automatically detect when foot plants should occur. Our method is more accurate than baseline methods that check the height or speed of the feet. These baseline methods perform especially poorly on noisy or imperfect data, requiring manual fixing. Once trained, our oracle is robust and can be used without manual clean-up, making it suitable for large databases of motion. After the foot plants are detected, we use an off-the-shelf inverse kinematics based method to maintain ground contact during each foot plant. Our foot plant detection mechanism coupled with an IK based fixer can be treated as a black box that produces natural-looking motion of the feet, making it suitable for interactive systems. We demonstrate several applications which would produce unrealistic motion without our method.

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This paper presents a simple and efficient technique for synthesizing high-fidelity motions by attaching, or splicing, the upper-body action of one motion example to the lower-body locomotion of another. Existing splicing algorithms do little more than copy degrees of freedom (DOFs) from one motion onto another. This naïve DOF replacement can produce unrealistic results because it ignores both physical and stylistic correlations between various joints in the body. Our approach uses spatial and temporal relationships found within the example motions to retain the overall posture of the upper-body action while adding secondary motion details appropriate to the timing and configuration of the lower body. By decoupling upper-body action from lower-body locomotion, our motion synthesis technique allows example motions to be captured independently and later combined to create new natural looking motions.

Fast and accurate recognition of motion data streams from gesture sensing and motion capture devices has many applications and is the focus of this paper. Based on the analysis of the geometric structures revealed by singular value decompositions (SVD) of motion data, a similarity measure is proposed for simultaneously segmenting motion streams and recognizing them. A direction identification approach is explored to further differentiate motions with similar data geometric structures. Experiments show that the proposed similarity measure can segment and recognize motion streams of variable lengths with high accuracy without knowing beforehand the number of motions in a stream.

To my wife Wei and my son Billy. iv Generating human motion that appears natural is a long standing problem in character animation. Researchers have explored many different approaches including physics-based simulation, optimization, and data-driven methods such as motion graphs and motion interpolation. One major difficulty in applying most of these approaches is the lack of an implementable definition of what it means for motion to be natural or human-like. In this thesis, we explore two techniques to fill this gap. The first technique creates a naturalness measure for quantifying natural human motion. The second technique involves a statistical analysis of human motion to compute aggregate statistics that are needed to guide animation algorithms for human figures toward natural looking solutions. A naturalness measure should be useful in verifying that a motion editing

Large amount of human motion capture data have been increasingly recorded and used in animation and gaming applications. Efficient retrieval of logically similar motions from a large data repository thereby serves as a fundamental basis for these motion data based applications. In this paper we present a perceptually consistent, example-based human motion retrieval approach that is capable of efficiently searching for and ranking similar motion sequences given a query motion input. Our method employs a motion pattern discovery and matching scheme that breaks human motions into a part-based, hierarchical motion representation. Building upon this representation, a fast string match algorithm is used for efficient runtime motion query processing. Finally, we conducted comparative user studies to evaluate the accuracy and perceptualconsistency of our approach by comparing it with the state of the art example-based human motion search algorithms.

Motion capture data is often used in movies and video games because it is able to realistically depict human motion. One of its greatest limitations is the inflexibility for reuse

Our goal in this project was to implement and validate the results of recent work employing statistical techniques to automatically determine a “naturalness” measure of human motion data. Using a training set of motion capture data (that in effect embodies our definition of naturalness), we learn several models to represent this natural motion, and test them on a variety of hand-selected positive and negative examples. The models we consider are the Naive Bayes model, mixtures of Gaussians, and hidden Markov models. We restrict our study to walking motions, but nonetheless achieve convincing and meaningful results, which are illustrated using ROC curves. In addition, we mention shortcomings of the original paper, and provide a few suggestions for further work.