Dance is believed to be important in the courtship of a variety of species, including humans, but nothing is known about what dance reveals about the underlying phenotypic--or genotypic--quality of the dancer. One measure of quality in evolutionary studies is the degree of bodily symmetry (fluctuating asymmetry, FA), because it measures developmental stability. Does dance quality reveal FA to the observer and is the effect stronger for male dancers than female? To answer these questions, we chose a population that has been measured twice for FA since 1996 (ref. 9) in a society (Jamaican) in which dancing is important in the lives of both sexes. Motion-capture cameras created controlled stimuli (in the form of videos) that isolated dance movements from all other aspects of visual appearance (including FA), and the same population evaluated these videos for dancing ability. Here we report that there are strong positive associations between symmetry and dancing ability, and these associations were stronger in men than in women. In addition, women rate dances by symmetrical men relatively more positively than do men, and more-symmetrical men value symmetry in women dancers more than do less-symmetrical men. In summary, dance in Jamaica seems to show evidence of sexual selection and to reveal important information about the dancer.

Figure 1Top: Simple input animation depicting hopscotch (a popular child game consisting of hops, broad jumps and a spin jump). Bottom: Synthesized realistic hopscotch animation. AbstractIn this paper we present a general method for rapid prototyping of realistic character motion. We solve for the natural motion from a simple animation provided by the animator. Our framework can be used to produce relatively complex realistic motion with little user effort.We describe a novel constraint detection method that automatically determines different constraints on the character by analyzing the input motion. We show that realistic motion can be achieved by enforcing a small set of linear and angular momentum constraints. This simplified approach helps us avoid the complexities of computing muscle forces. Simpler dynamic constraints also allow us to generate animations of models with greater complexity, performing more intricate motions. Finally, we show that by learning a small set of key parameters that describe a character pose we can help a non-skilled animator rapidly create realistic character motion.

This paper presents a novel physics-based representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task. When used in a spacetime optimization framework, the parameters of this model define a wide range of styles of natural human movement.Due to the complexity of biological motion, these style parameters are too difficult to design by hand. To address this, we introduce Nonlinear Inverse Optimization, a novel algorithm for estimating optimization parameters from motion capture data. Our method can extract the physical parameters from a single short motion sequence. Once captured, this representation of style is extremely flexible: motions can be generated in the same style but performing different tasks, and styles may be edited to change the physical properties of the body.

This paper presents a system for rapid editing of highly dynamic motion capture data. At the heart of this system is an optimization algorithm that can transform the captured motion so that it satisfies high-level user constraints while enforcing that the linear and angular momentum of the motion remain physically plausible. Unlike most previous approaches to motion editing, our algorithm does not require pose specification or model reduction, and the user only need specify high-level changes to the input motion. To preserve the dynamic behavior of the input motion, we introduce a spline-based parameterization that matches the linear and angular momentum patterns of the motion capture data. Because our algorithm enables rapid convergence by presenting a good initial state of the optimization, the user can efficiently generate a large number of realistic motions from a single input motion. The algorithm can then populate the dynamic space of motions by simple interpolation, effectively parameterizing the space of realistic motions. We show how this framework can be used to produce an effective interface for rapid creation of dynamic animations, as well as to drive the dynamic motion of a character in real-time.