This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondarymotion to the animations with springmass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.

There are many applications that demand large quantities of natural looking motion. It is difficult to synthesize motion that looks natural, particularly when it is people who must move. In this paper, we present a framework that generates human motions by cutting and pasting motion capture data. Selecting a collection of clips that yields an acceptable motion is a combinatorial problem that we manage as a randomized search of a hierarchy of graphs. This approach can generate motion sequences that satisfy a variety of constraints automatically. The motions are smooth and human-looking. They are generated in real time so that we can author complex motions interactively. The algorithm generates multiple motions that satisfy a given set of constraints, allowing a variety of choices for the animator. It can easily synthesize multiple motions that interact with each other using constraints. This framework allows the extensive re-use of motion capture data for new purposes.

This paper describes an algorithm for automatically adapting existing simulated behaviors to new characters. Animating a new character is difficult because a control system tuned for one character will not, in general, work on a character with different limb lengths, masses, or moments of inertia. The algorithm presented here adapts the control system to a new character in two stages. First, the control system parameters are scaled based on the sizes, masses, and moments of inertia of the new and the original characters. Then a subset of the parameters is fine-tuned using a search process based on simulated annealing. To demonstrate the effectiveness of this approach, we animate the running motion of a woman, child, and imaginary character by modifying the control system for a man. We also animate the bicycling motion of a second imaginary character by modifying the control system for a man. We evaluate the results of this approach by comparing the motion of the simulated human runners...

Seemingly simple behaviors such as human walking are difficult to model because of their inherent instability. Kinematic animation techniques can freely ignore such intrinsically dynamic problems, but they therefore also miss modeling important motion characteristics. On the other hand, the effect of balancing can emerge in a physically-based animation, but it requires computing delicate control strategies. We propose an alternative method that adds closedloop feedback to open-loop periodic motions. We then apply our technique to create robust walking gaits for a fully-dynamic 19 degree -of-freedom human model. Important global characteristics such as direction, speed and stride rate can be controlled by changing the open-loop behavior alone or through simple control parameters, while continuing to employ the same local stabilization technique. Among other features, our dynamic "human" walking character is thus able to follow desired paths specified by the animator. Keywords: control...

Birds, fish, and many other animals travel as a flock, school, or herd. Animals in these groups must remain in close proximity while avoiding collisions with neighbors and with obstacles. We would like to reproduce this behavior for groups of artificial creatures with significant dynamics. In this paper we describe an algorithm for creatures that move as a group and evaluate the performance of the algorithm with three simulated systems: legged robots, human-like bicycle riders, and point-mass systems. Both the legged robots and the bicyclists are dynamic simulations that must control balance, facing direction, and forward speed as well as movement with the group. The point-mass systems have minimal dynamics and are included to facilitate our understanding of the effects of the dynamics on the performance of the algorithms. Introduction To run as a group, animals must remain in close proximity while changing direction and velocity and avoiding collisions with other group members and o...

Abstract — In this paper, we report on our research for learning biped locomotion from human demonstration. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a CPG of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through the movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithm based on phase resetting and entrainment of oscillators. Numerical simulations demonstrate the effectiveness of the proposed locomotion controller. I.

This paper describes a fast technique for modifying motion sequences for complex articulated mechanisms in a way that preserves physical properties of the motion. This technique is relevant to the problem of teaching motion tasks by demonstration, because it allows a single example to be adapted to a range of situations. Motion may be obtained from any source; for example, it may be captured from a human user. A model of applied forces is extracted from the motion data, and forces are scaled to achieve new goals. Each scaled force model is checked to ensure that frictional and kinematic constraints are maintained for a rigid body approximation of the character. Scale factors can be obtained in closed form, and constraints can be approximated analytically, making motion editing extremely fast. To demonstrate the effectiveness of this approach, we show that a variety of simulated jumps can be created by modifying a single keyframed jumping motion. We also scale a simulated running motion...

The motion of a human platform diver was simulated using a dynamic model and a control system. The dynamic model has 32 actuated degrees of freedom and dynamic parameters within the range of those reported in the literature for humans. The control system uses algorithms for balance, jumping, and twisting to initiate the dive, sequences of desired values for proportional--derivative servos to perform the aerial portion of the dive, and a state machine to sequence the actions throughout the dive. The motion of the simulated diver closely resembles video footage of dives performed by human athletes. The control and simulation techniques presented in this paper are useful for providing realistic motion for synthetic actors in computer animations and virtual environments and may someday be useful for analysis of sports performance. 1. Introduction In this paper, we explore dynamic simulation as a technique for generating animations of an Olympic sport, platform diving. The simulated diver ...

Many classes of applications require multiagent navigation control algorithms to specify the movements and actions of heterogeneous groups containing thousands of characters. The scale and complexity of these interacting character groups require navigation control algorithms that are both generalizable and specifically tuned to particular character platforms. We propose a technique called simulation level of detail (LOD) that provides a simulation-based interface between navigation control algorithms and the specific mobile characters on which they operate. A simulation LOD e#ciently models a character's ability to move given its dynamic state and provides this simplified version of the character to navigation controllers for use in run-time search algorithms that compute locomotion actions. We develop our simulation LOD algorithms on groups of physically simulated human and alien bicyclists and demonstrate reusable controllers that provide improvements in path following and herding tasks.

Data-driven techniques for animation, where motion captured from a human performer is "played back" through an animated human character, are becoming increasingly popular in computer graphics. These techniques are appealing because the resulting motion often retains the natural feel of the original performance. Data-driven techniques have the disadvantage, however, that it is difficult to preserve this natural feel when the motion is edited, especially when it is edited by a software program adapting the motion for new uses (e.g. to animate a character traveling over uneven terrain). In fact, many current techniques for motion editing do not preserve basic laws of physics. We present a dynamic filtering technique for improving the physical realism of motion for animation. As a specific example, we show that this filter can improve the physical realism of automatically generated motion transitions. Our test case is the physically challenging transition contained in a double kick constructed by merging two single kicks.