Motion track: Visualizing variations of human motion data

This paper proposes a novel visualization approach, which can depict the variations between different human motion data. This is achieved by representing the time dimension of each animation sequence with a sequential curve in a locality-preserving reference 2D space, called the motion track representation. The principal advantage of this representation over standard representations of motion capture data - generally either a keyframed timeline or a 2D motion map in its entirety - is that it maps the motion differences along the time dimension into parallel perceptible spatial dimensions but at the same time captures the primary content of the source data. Latent semantic differences that are difficult to be visually distinguished can be clearly displayed, favoring effective summary, clustering, comparison and analysis of motion database.