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: Optimal and sub-optimal solutions for walking a given distance (left) and for picking up an object (right). Many compelling applications would become feasible if novice users had the ability to synthesize high quality human motion based only on a simple sketch and a few easily specified constraints. We approach this problem by representing the desired motion as an interpolation of two time-scaled paths through a motion graph. The graph is constructed to support interpolation and pruned for efficient search. We use an anytime version of A ∗ search to find a globally optimal solution in this graph that satisfies the user’s specification. Our approach retains the natural transitions of motion graphs and the ability to synthesize physically realistic variations provided by interpolation. We demonstrate the power of this approach by synthesizing optimal or near optimal motions that include a variety of behaviors in a single motion.

Figure 1: By modeling user behavior and not thresholding transitions, we create a high overall quality on-line motion generator suitable for directly controlled characters. Left, a screen capture. Middle, users control the character with a gamepad. Right, characters must respond immediately to user input, lest they run afoul of environmental hazards. In game environments, animated character motion must rapidly adapt to changes in player input – for example, if a directional signal from the player’s gamepad is not incorporated into the character’s trajectory immediately, the character may blithely run off a ledge. Traditional schemes for data-driven character animation lack the split-second reactivity required for this direct control; while they can be made to work, motion artifacts will result. We describe an on-line character animation controller that assembles a motion stream from short motion fragments, choosing each fragment based on current player input and the previous fragment. By adding a simple model of player behavior we are able to improve

Figure 1: Motions computed from spatial-temporal constraints. In this paper, we present a technique for generating animation from a variety of user-defined constraints. We pose constraint-based motion synthesis as a maximum a posterior (MAP) problem and develop an optimization framework that generates natural motion satisfying user constraints. The system automatically learns a statistical dynamic model from motion capture data and then enforces it as a motion prior. This motion prior, together with user-defined constraints, comprises a trajectory optimization problem. Solving this problem in the low-dimensional space yields optimal natural motion that achieves the goals specified by the user. We demonstrate the effectiveness of this approach by generating whole-body and facial motion from a variety of spatial-temporal constraints.

Real-time adaptation of a motion capture sequence to virtual environments with physical perturbations requires robust control strategies. This paper describes an optimal feedback controller for motion tracking that allows for on-the-fly re-planning of long-term goals and adjustments in the final completion time. We first solve an offline optimal trajectory problem for an abstract dynamic model that captures the essential relation between contact forces and momenta. A feedback control policy is then derived and used to simulate the abstract model online. Simulation results become dynamic constraints for online reconstruction of full-body motion from a reference. We applied our controller to a wide range of motions including walking, long stepping, and a squat exercise. Results show that our controllers are robust to large perturbations and changes in the environment.

Realistic and directable humanlike characters are an ongoing goal in animation. Motion graph data structures hold much promise for achieving this goal; however, the quality of the results obtainable from a motion graph may not be easy to predict from its input motion clips. This paper describes a method for using task-based metrics to evaluate the capability of a motion graph to create the set of animations required by a particular application. We examine this capability for typical motion graphs across a range of tasks and environments. We find that motion graph capability degrades rapidly with increases in the complexity of the target environment or required tasks, and that addressing deficiencies in a brute-force manner tends to lead to large, unwieldy motion graphs. The results of this method can be used to evaluate the extent to which a motion graph will fulfill the requirements of a particular application, lessening the risk of the data structure performing poorly at an inopportune moment. The method can also be used to characterize the deficiencies of motion graphs whose performance will not be sufficient, and to evaluate the relative effectiveness of different options for improving those motion graphs.

Modeling the large space of possible human motions requires scalable techniques. Generalizing from example motions or example controllers is one way to provide the required scalability. We present techniques for generalizing a controller for physics-based walking to significantly different tasks, such as climbing a large step up, or pushing a heavy object. Continuation methods solve such problems using a progressive sequence of problems that trace a path from an existing solved problem to the final desired-butunsolved problem. Each step in the continuation sequence makes progress towards the target problem while further adapting the solution. We describe and evaluate a number of choices in applying continuation methods to adapting walking gaits for tasks involving interaction with the environment. The methods have been successfully applied to automatically adapt a regular cyclic walk to climbing a 65cm step, stepping over a 55cm sill, pushing heavy furniture, walking up steep inclines, and walking on ice. The continuation path further provides parameterized solutions to these problems.

Abstract — In order to interact with human environments, humanoid robots require safe and compliant control which can be achieved through force-controlled joints. In this paper, full body step recovery control for robots with force-controlled joints is achieved by adding model-based feed-forward controls. Push Recovery Model Predictive Control (PR-MPC) is presented as a method for generating full-body step recovery motions after a large disturbance. Results are presented from experiments on the Sarcos Primus humanoid robot that uses hydraulic actuators instrumented with force feedback control. I.

Complex acrobatic stunts, such as double or triple flips, can be performed only by highly skilled athletes. On the other hand, simpler tricks, such as single-flip jumps, are relatively easy to master. We present a method for creating complex, multi-flip ballistic motions from simple, single-flip jumps. Our approach also allows an animator to interact with the system by introducing modifications to a ballistic phase of a motion. Our method automatically adjusts motion trajectories, to assure physical validity of the motion after the modifications. The presented technique is efficient and produces physically valid results without resorting to computationally expensive optimization. To validate our approach we present the results of a study of user sensitivity to errors in angular momentum and take-off angle. The study shows that small changes of these parameters introduced by our method are not perceptible to a viewer.

the competition by physically-based animation It is difficult to create scenes where multiple avatars are fighting / competing with each other. Manually creating the motions of avatars is time consuming due to the correlation of the movements between the avatars. Capturing the motions of multiple avatars is also difficult as it requires a huge amount of post-processing. In this paper, we propose a new method to generate a realistic scene of avatars densely interacting in a competitive environment. The motions of the avatars are considered to be captured individually, which will increase the easiness of obtaining the data. We propose a new algorithm called the temporal expansion approach which maps the continuous time action plan to a discrete space such that turnbased evaluation methods can be used. As a result, many mature algorithms in game such as the min-max search and α − β pruning can be applied. Using our method, avatars will plan their strategies taking into account the reaction of the opponent. Fighting scenes with multiple avatars are generated to demonstrate the effectiveness of our algorithm. The proposed method can also be applied to other kinds of continuous activities that require strategy planning such as sport games. CR Categories: I.3.6 [Methodology and Techniques]: Three-