Analyzing motion data is a critical step when building meaningful locomotive motion datasets. This can be done by labeling motion capture data and inspecting it, through a planned motion capture
session or by carefully selecting locomotion clips from a public dataset. These analyses, however, have no clear definition of coverage, making it harder to diagnose when something goes wrong, such as a virtual character not being able to perform an action or not moving at a given speed. This issue is compounded by the large amount of information present in motion capture data, which poses a challenge when trying to interpret it. This work provides a visualization and an optimization method to streamline the process
of crafting locomotive motion datasets. It provides a more grounded approach towards locomotive motion analysis by calculating different quality metrics, such as: demarcating coverage in terms of both linear and angular speeds, frame use frequency in each animation clip, deviation from the planned path, number of transitions, number of used vs. unused animations and transition cost. By using these metrics as a comparison mean for different motion datasets, our approach is able to provide a less subjective alternative to the modification and analysis of motion datasets, while improving interpretability.