Figure 1: A motion-captured character performs a jumping kick. His clothing is dynamically remeshed to capture detail such as wrinkles, while having larger elements in smooth areas. Here and elsewhere in the paper, large elements are shown in blue, small equilateral elements in red, and anisotropic elements in yellow. AbstractWe present a technique for cloth simulation that dynamically refines and coarsens triangle meshes so that they automatically conform to the geometric and dynamic detail of the simulated cloth. Our technique produces anisotropic meshes that adapt to surface curvature and velocity gradients, allowing efficient modeling of wrinkles and waves. By anticipating buckling and wrinkle formation, our technique preserves fine-scale dynamic behavior. Our algorithm for adaptive anisotropic remeshing is simple to implement, takes up only a small fraction of the total simulation time, and provides substantial computational speedup without compromising the fidelity of the simulation. We also introduce a novel technique for strain limiting by posing it as a nonlinear optimization problem. This formulation works for arbitrary non-uniform and anisotropic meshes, and converges more rapidly than existing solvers based on Jacobi or Gauss-Seidel iterations.

In this paper, we augment existing techniques for simulating flexible objects to include models for crack initiation and propagation in three-dimensional volumes. By analyzing the stress tensors computed over a finite element model, the simulation determines where cracks should initiate and in what directions they should propagate. We demonstrate our results with animations of breaking bowls, cracking walls, and objects that fracture when they collide. By varying the shape of the objects, the material properties, and the initial conditions of the simulations, we can create strikingly different effects ranging from a wall that shatters when it is hit by a wrecking ball to a bowl that breaks in two when it is dropped on edge.

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 secondary motion 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.

Traditionally, shape transformation using implicit functions is performed in two distinct steps: 1) creating two implicit functions, and 2) interpolating between these two functions. We present a new shape transformation method that combines these two tasks into a single step. We create a transformation between two Ndimensional objects by casting this as a scattered data interpolation problem in N + 1 dimensions. For the case of 2D shapes, we place all of our data constraints within two planes, one for each shape. These planes are placed parallel to one another in 3D. Zero-valued constraints specify the locations of shape boundaries and positivevalued constraints are placed along the normal direction in towards the center of the shape. We then invoke a variational interpolation technique (the 3D generalization of thin-plate interpolation), and this yields a single implicit function in 3D. Intermediate shapes are simply the zero-valued contours of 2D slices through this 3D function. Shape transformation between 3D shapes can be performed similarly by solving a 4D interpolation problem. To our knowledge, ours is the first shape transformation method to unify the tasks of implicit function creation and interpolation. The transformations produced by this method appear smooth and natural, even between objects of differing topologies. If desired, one or more additional shapes may be introduced that influence the intermediate shapes in a sequence. Our method can also reconstruct surfaces from multiple slices that are not restricted to being parallel to one another.

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