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.

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.

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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.

This paper presents a novel constraint-based motion editing technique. On the basis of animator-specified kinematic and dynamic constraints, the method converts a given captured or animated motion to a physically plausible motion. In contrast to previous methods using spacetime optimization, we cast the motion editing problem as a constrained state estimation problem based on the per-frame Kalman filter framework. The method works as a filter that sequentially scans the input motion to produce a stream of output motion frames at a stable interactive rate. Animators can tune several filter parameters to adjust to different motions, or can turn the constraints on or off based on their contributions to the final re- sult. One particularly appealing feature of the proposed technique is that animators find it very scalable and intuitive. Experiments on various systems show that the technique processes the motions of a human with 54 degrees of freedom at about 150 fps when only kinematic constraints are applied, and at about 10 fps when both kinematic and dynamic constraints are applied. Experiments on various types of motion show that the proposed method produces remarkably realistic animations.

Motion capture can be an effective method of creating realistic human motion for animation. Unfortunately, the quality demands for animation place challenging demands on a capture system. To date, capture solutions that meet these demands have required specialized hardware that is invasive and expensive. Computer vision could make animation data much easier to obtain. Unfortunately, current techniques fall short of the demands of animation applications. In this paper, we will explore why the demands of animation lead to a particularly difficult challenge for capture techniques. We present a constraint-based methodology for reconstructing the 3D motion given image observations, and use this as a tool for understanding the problem. Synthetic experiments confirm that these situations would arise in practice. The experiments show how even simple visual tracking information can be used to create human motion but even with perfect tracking, incorrect reconstructions are not only possible but inevitable.

In this paper, we present an interactive motion deformation method to modify animations so that they satisfy a set of prioritized constraints. Our approach successfully handles the problem of retargetting, adjusting a motion, as well as adding significant changes to preexisting animations. We introduce the concept of prioritized constraints to avoid tweaking issues for competing constraints. Each frame is individually and smoothly adjusted to enforce a set of prioritized constraints. The iterative construction of the solution channels the convergence through intermediate solutions, enforcing the highest prioritized constraints first. In addition, we propose a new, simple formulation to control the position of the center of mass so that the resulting motions are physically plausible. Finally, we demonstrate that our method can address a wide range of motion editing problems. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional

Motion is the center of attention in many applications of computer graphics. Skeletal motion for articulated characters can be processed and altered in a great variety of ways to increase the versatility of each motion clip. However, analogous techniques have not yet been developed for free-form deforming surfaces like cloth and faces. Given the time consuming nature of producing each free-form motion clip, the ability to alter and reuse free-form motion would be very desirable. We present a novel method for processing free-form motion that opens up a broad range of possible motion alterations, including motion blending, keyframe insertion, and temporal signal processing. Our method is based on a simple, yet powerful, differential surface representation that is invariant under rotation and translation, and which is well suited for surface editing in both space and time.

In this paper we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the directional change in motion data. The method is based on entropy metrics, specifically the mutual information measure. Displacement histograms between frames are evaluated and the mutual information metric is employed in order to calculate the inter-frame dependency. The most significant keypose identifies the largest directional change in the motion data. This will have the lowest mutual information level from all the candidate keyposes. Less significant keyposes are then listed with higher mutual information levels. The results show that the method has higher sensitivity in the directional change than methods based on the magnitude of the velocity alone. This method is intended to provide a summary of a motion clip by ranked keyposes, which is highly useful in motion browsing and motion retrieve database system.

Graph-based approaches for sequencing motion capture data have produced some of the most realistic and controllable character motion to date. Most previous graph-based approaches have employed a run-time global search to find paths through the motion graph that meet user-defined constraints such as a desired locomotion path. Such searches do not scale well to large numbers of characters. In this paper, we describe a locomotion approach that benefits from the realism of graph-based approaches while maintaining basic user control and scaling well to large numbers of characters. Our approach is based on precomputing multiple least cost sequences from every state in a state-action graph. We store these precomputed sequences in a data structure called a mobility map and perform a local search of this map at run-time to generate motion sequences in real time that achieve user constraints in a natural manner. We demonstrate the quality of the motion through various example locomotion tasks including target tracking and collision avoidance. We demonstrate scalability by animating crowds of up to 150 rendered articulated walking characters at real-time rates.