  This paper presents GestureMap, a visual analytics tool for gesture
elicitation which directly visualises the space of gestures. Concretely, a
Variational Autoencoder embeds gestures recorded as 3D skeletons on an
interactive 2D map. GestureMap further integrates three computational
capabilities to connect exploration to quantitative measures: Leveraging DTW
Barycenter Averaging (DBA), we compute average gestures to 1) represent gesture
groups at a glance; 2) compute a new consensus measure (variance around average
gesture); and 3) cluster gestures with k-means. We evaluate GestureMap and its
concepts with eight experts and an in-depth analysis of published data. Our
findings show how GestureMap facilitates exploring large datasets and helps
researchers to gain a visual understanding of elicited gesture spaces. It
further opens new directions, such as comparing elicitations across studies. We
discuss implications for elicitation studies and research, and opportunities to
extend our approach to additional tasks in gesture elicitation.
