http://hdl.handle.net/1721.1/29806 http://hdl.handle.net/1721.1/41946 Guided Time Warping for Motion Editing Hsu, Eugene Silva, Marco da Popovic, Jovan Time warping allows users to modify timing without affecting poses. It has many applications in animation systems for motion editing, such as refining motions to meet new timing constraints or modifying the acting of animated characters. However, time warping typically requires many manual adjustments to achieve the desired results. We present a technique which simplifies this process by allowing time warps to be guided by a provided reference motion. Given few timing constraints, it computes a warp that both satisfies these constraints and maximizes local timing similarities to the reference. The algorithm is fast enough to incorporate into standard animation workflows. We apply the technique to two common tasks: preserving the natural timing of motions under new time constraints and modifying the timing of motions for stylistic effects. http://hdl.handle.net/1721.1/41945 Style Translation for Human Motion Hsu, Eugene Pulli, Kari Popovic, Jovan Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of interactive applications, which require rapid processing of captured performances. Our solution learns to translate by analyzing differences between performances of the same content in input and output styles. It relies on a novel correspondence algorithm to align motions, and a linear time-invariant model to represent stylistic differences. Once the model is estimated with system identification, our system is capable of translating streaming input with simple linear operations at each frame. http://hdl.handle.net/1721.1/41944 Example-Based Control of Human Motion Hsu, Eugene Gentry, Sommer Popovic, Jovan In human motion control applications, the mapping between a control specification and an appropriate target motion often defies an explicit encoding. We present a method that allows such a mapping to be defined by example, given that the control specification is recorded motion. Our method begins by building a database of semantically meaningful instances of the mapping, each of which is represented by synchronized segments of control and target motion. A dynamic programming algorithm can then be used to interpret an input control specification in terms of mapping instances. This interpretation induces a sequence of target segments from the database, which is concatenated to create the appropriate target motion. We evaluate our method on two examples of indirect control. In the first, we synthesize a walking human character that follows a sampled trajectory. In the second, we generate a synthetic partner for a dancer whose motion is acquired through motion capture. http://hdl.handle.net/1721.1/41940 A Note on Perturbation Results for Learning Empirical Operators Rosasco, Lorenzo Belkin, Mikhail De Vito, Ernesto A large number of learning algorithms, for example, spectralclustering, kernel Principal Components Analysis and many manifoldmethods are based on estimating eigenvalues and eigenfunctions ofoperators defined by a similarity function or a kernel, given empiricaldata. Thus for the analysis of algorithms, it is an important problem tobe able to assess the quality of such approximations. The contributionof our paper is two-fold:1. We use a technique based on a concentration inequality for Hilbertspaces to provide new much simplified proofs for a number of results inspectral approximation.2. Using these methods we provide several new results for estimatingspectral properties of the graph Laplacian operator extending andstrengthening results from [26].