Character animation in video games-whether manually keyframed or motion captured-has traditionally relied on codifying skeletons early in a game's development, and creating animations rigidly tied to these fixed skeleton morphologies. This paper introduces a novel system for animating characters whose morphologies are unknown at the time the animation is created. Our authoring tool allows animators to describe motion using familiar posing and key-framing methods. The system records the data in a morphology-independent form, preserving both the animation's structural relationships and its stylistic information. At runtime, the generalized data are applied to specific characters to yield pose goals that are supplied to a robust and efficient inverse kinematics solver. This system allows us to animate characters with highly varying skeleton morphologies that did not exist when the animation was authored, and, indeed, may be radically different than anything the original animator envisioned.
Character animation in video games-whether manually keyframed or motion captured-has traditionally relied on codifying skeletons early in a game's development, and creating animations rigidly tied to these fixed skeleton morphologies. This paper introduces a novel system for animating characters whose morphologies are unknown at the time the animation is created. Our authoring tool allows animators to describe motion using familiar posing and key-framing methods. The system records the data in a morphology-independent form, preserving both the animation's structural relationships and its stylistic information. At runtime, the generalized data are applied to specific characters to yield pose goals that are supplied to a robust and efficient inverse kinematics solver. This system allows us to animate characters with highly varying skeleton morphologies that did not exist when the animation was authored, and, indeed, may be radically different than anything the original animator envisioned.
Spectral matching algorithms, such as the Spectral Angle Mapper (SAM), utilize the spectral similarity between individual image pixel spectra and a spectral reference library with known components. Here, we illustrate and quantify the effects that different sources of reference libraries have on SAM classification results. Synthetic images of three mineral endmembers were classified by using reference libraries derived from airborne hyperspectral imagery, ground spectra (Portable Infrared Mineral Analyser), and from a standard library (United States Geologic Survey). Results show that the source of the reference library strongly influences the classification results if all available wavelengths are used. This effect can be partially neutralized by using appropriate preprocessing methods. Two different types of spectral subsetting of the data, two types of continuum removal, and a combination thereof were tested. Best results were achieved by using a feature subset (i.e., limiting the input wavelengths to the diagnostic absorption features). This increased the average classification accuracy from 74% to 95% (ground spectral library) and from 68% to 94% (standard library).
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