Controllable, reactive human motion is essential in many video games and training environments. Characters in these applications often perform tasks based on modified motion data, but response to unpredicted events is also important in order to maintain realism. We approach the problem of motion synthesis for interactive, humanlike characters by combining dynamic simulation and human motion capture data. Our control systems use trajectory tracking to follow motion capture data and a balance controller to keep the character upright while modifying sequences from a small motion library to accomplish specified tasks, such as throwing punches or swinging a racket. The system reacts to forces computed from a physical collision model by changing stiffness and damping terms. The freestanding, simulated humans respond automatically to impacts and smoothly return to tracking. We compare the resulting motion with video and recorded human data.
We demonstrate a real-time simulation system capable of automatically balancing a standing character, while at the same time tracking a reference motion and responding to external perturbations. The system is general to non-human morphologies and results in natural balancing motions employing the entire body (for example, wind-milling). Our novel balance routine seeks to control the linear and angular momenta of the character. We demonstrate how momentum is related to the center of mass and center of pressure of the character and derive control rules to change these centers for balance. The desired momentum changes are reconciled with the objective of tracking the reference motion through an optimization routine which produces target joint accelerations. A hybrid inverse/forward dynamics algorithm determines joint torques based on these joint accelerations and the ground reaction forces. Finally, the joint torques are applied to the free-standing character simulation. We demonstrate results for following both motion capture and keyframe data as well as both human and non-human morphologies in presence of a variety of conditions and disturbances.
Time series motifs are approximately repeated patterns found within the data. Such motifs have utility for many data mining algorithms, including rule-discovery, novelty-detection, summarization and clustering. Since the formalization of the problem and the introduction of efficient linear time algorithms, motif discovery has been successfully applied to many domains, including medicine, motion capture, robotics and meteorology.In this work we show that most previous applications of time series motifs have been severely limited by the definition's brittleness to even slight changes of uniform scaling, the speed at which the patterns develop. We introduce a new algorithm that allows discovery of time series motifs with invariance to uniform scaling, and show that it produces objectively superior results in several important domains. Apart from being more general than all other motif discovery algorithms, a further contribution of our work is that it is simpler than previous approaches, in particular we have drastically reduced the number of parameters that need to be specified.
Despite recent advances in the study of animal flight, the biomechanical determinants of maneuverability are poorly understood. It is thought that maneuverability may be influenced by intrinsic body mass and wing morphology, and by physiological muscle capacity, but this hypothesis has not yet been evaluated because it requires tracking a large number of free flight maneuvers from known individuals. We used an automated tracking system to record flight sequences from 20 Anna's hummingbirds flying solo and in competition in a large chamber. We found that burst muscle capacity predicted most performance metrics. Hummingbirds with higher burst capacity flew with faster velocities, accelerations, and rotations, and they used more demanding complex turns. In contrast, body mass did not predict variation in maneuvering performance, and wing morphology predicted only the use of arcing turns and high centripetal accelerations. Collectively, our results indicate that burst muscle capacity is a key predictor of maneuverability.DOI: http://dx.doi.org/10.7554/eLife.11159.001
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