Supervised learning of motion style for real-time synthesis of 3D character animations

In this paper, we present a supervised learning framework to learn a probabilistic mapping from values of a low-dimensional style variable, which defines the characteristics of a certain kind of 3D human motion such as walking or boxing, to high-dimensional vecotrs defining 3D poses. All possible values of the style variable span an Euclidean space called style space. The supervised learning framework guarantees that each dimension of style space corresponds to a certain aspect of the motion characteristics, such as body height and pace length, so the user can precisely define a 3D pose by locating a point in the style space. Moreover, every curve in the Euclidean style space corresponds to a smooth motion sequence. We developed a graphical user interface program, with which, users simply points mouse cursor in the style space to define a 3D pose and drags mouse cursor to synthesis 3D animations in real-time.