Animating an articulated 3D character currently requires manual rigging to specify its internal skeletal structure and to define how the input motion deforms its surface. We present a method for animating characters automatically. Given a static character mesh and a generic skeleton, our method adapts the skeleton to the character and attaches it to the surface, allowing skeletal motion data to animate the character. Because a single skeleton can be used with a wide range of characters, our method, in conjunction with a library of motions for a few skeletons, enables a user-friendly animation system for novices and children. Our prototype implementation, called Pinocchio, typically takes under a minute to rig a character on a modern midrange PC.
Commercial motion-capture systems produce excellent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motions almost anywhere. This wearable system gathers ultrasonic time-of-flight and inertial measurements with a set of inexpensive miniature sensors worn on the garment. After recording, the information is combined using an Extended Kalman Filter to reconstruct joint configurations of a body. Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings. Although our prototype does not reliably recover the global transformation, we show that the resulting motions are visually similar to the original ones, and that the combined acoustic and inertial system reduces the drift commonly observed in purely inertial systems. Our final results suggest that this system could become a versatile input device for a variety of augmented-reality applications. Figure 1: Traditional motion-capture systems excel at recording motions within lab-like environments but struggle with recording outdoor activities such as skiing, biking, and driving. This limitation led us to design a wearable motion-capture system that records human activity in both indoor and outdoor environments. AbstractCommercial motion-capture systems produce excellent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motions almost anywhere. This wearable system gathers ultrasonic time-of-flight and inertial measurements with a set of inexpensive miniature sensors worn on the garment. After recording, the information is combined using an Extended Kalman Filter to reconstruct joint configurations of a body. Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings. Although our prototype does not reliably recover the global transformation, we show that the resulting motions are visually similar to the original ones, and that the combined acoustic and inertial system reduces the drift commonly observed in purely inertial systems. Our final results suggest that this system could become a versatile input device for a variety of augmented-reality applications.
Object deformation with linear blending dominates practical use as the fastest approach for transforming raster images, vector graphics, geometric models and animated characters. Unfortunately, linear blending schemes for skeletons or cages are not always easy to use because they may require manual weight painting or modeling closed polyhedral envelopes around objects. Our goal is to make the design and control of deformations simpler by allowing the user to work freely with the most convenient combination of handle types. We develop linear blending weights that produce smooth and intuitive deformations for points, bones and cages of arbitrary topology. Our weights, called bounded biharmonic weights, minimize the Laplacian energy subject to bound constraints. Doing so spreads the influences of the controls in a shape-aware and localized manner, even for objects with complex and concave boundaries. The variational weight optimization also makes it possible to customize the weights so that they preserve the shape of specified essential object features. We demonstrate successful use of our blending weights for real-time deformation of 2D and 3D shapes.
We propose a novel representation of motion data and control that enables characters with both highly agile responses to user input and natural handling of arbitrary external disturbances. The representation organizes motion data as samples in a high dimensional generalization of a vector field we call a 'motion field'. Our runtime motion synthesis mechanism freely 'flows' in the motion field and is capable of creating novel and natural motions that are highlyresponsive to the real time user input, and generally not explicitly specified in the data.
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