Abstract: A computer-implemented method includes identifying a representation of a feature of an animated character by inverting an skinned representation of the feature in one position. The inversion includes a non-linear inversion of the skinned representation of the feature. The method also includes skinning the identified feature representation to produce the animated character in another position.

Abstract: Systems and methods are described, which create a mapping from a space of a source object (e.g., source facial expressions) to a space of a target object (e.g., target facial expressions). In certain implementations, the mapping is learned based a training set composed of corresponding shapes (e.g. facial expressions) in each space. The user can create the training set by selecting expressions from, for example, captured source performance data, and by sculpting corresponding target expressions. Additional target shapes (e.g., target facial expressions) can be interpolated and extrapolated from the shapes in the training set to generate corresponding shapes for potential source shapes (e.g., facial expressions).

Abstract: Among other disclosed subject matter, a computer-implemented method for generating a surface representation of an item includes identifying, for a point on an item in an animation process, at least first and second transformation points corresponding to respective first and second transformations of the point. Each of the first and second transformations represents an influence on a location of the point of respective first and second joints associated with the item. The method includes determining an axis for a cylindrical coordinate system using the first and second transformations. The method includes performing an interpolation of the first and second transformation points in the cylindrical coordinate system to obtain an interpolated point. The method includes recording the interpolated point in a surface representation of the item in the animation process.

Abstract: Systems and methods are described, which create a mapping from a space of a source object (e.g., source facial expressions) to a space of a target object (e.g., target facial expressions). In certain implementations, the mapping is learned based a training set composed of corresponding shapes (e.g. facial expressions) in each space. The user can create the training set by selecting expressions from, for example, captured source performance data, and by sculpting corresponding target expressions. Additional target shapes (e.g., target facial expressions) can be interpolated and extrapolated from the shapes in the training set to generate corresponding shapes for potential source shapes (e.g., facial expressions).