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.
We present a data-driven method for automatically cropping photographs to be well-composed and aesthetically pleasing. Our method matches the composition of an amateur's photograph to an expert's using point correspondences. The correspondences are based on a novel high-level local descriptor we term the 'Object Context'. Object Context is an extension of Shape Context: it is a descriptor encoding which objects and scene elements surround a given point. By searching a database of expertly composed images, we can find a crop window which makes an amateur's photograph closely match the composition of a database exemplar. We cull irrelevant matches in the database efficiently using a global descriptor which encodes the objects in the scene. For images with similar content in the database, we efficiently search the space of possible crops using generalized Hough voting. When comparing the result of our algorithm to expert crops, our crop windows overlap the expert crops by 83.6%. We also perform a user study which shows that our crops compare favourably to an expert humans' crops.
Figure 1: Left: Three frames from an adaptive simulation of a flag. Right: The optimal fixed-topology mesh resolves all details in the animation, including those in frames not shown here. Colors indicate triangle size and shape (blue: large, red: small, yellow: anisotropic). AbstractWe describe a method for converting an adaptively remeshed simulation of cloth into an animated mesh with fixed topology. The topology of the mesh may be specified by the user or computed automatically. In the latter case, we present a method for computing the optimal output mesh, that is, a mesh with spatially varying resolution which is fine enough to resolve all the detail present in the animation. This technique allows adaptive simulations to be easily used in applications that expect fixed-topology animated meshes.
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