Constrained Dynamic Mode Decomposition.

Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no means to influence the core algorithm. A method that also belongs to this class is Dynamic Mode Decomposition (DMD), a spectral decomposition method that extracts spatio-temporal patterns from data.

Unsupervised and Generic Short-Term Anticipation of Human Body Motions.

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions.

Cloth Animation Retrieval Using a Motion-Shape Signature.

In cloth simulation, the behavior of textiles largely depends on initial conditions, parameters, and simulation techniques. Usually, several combinations of those aspects are altered until a simulation setting is found to create a satisfying animation. However, if an initial condition, such as a collision object, is changed afterward or the cloth behavior is transferred to a different scene, the existing set of simulation parameters could no longer be suitable for the desired look.