  In many contexts, creating mappings for gestural interactions can form part
of an artistic process. Creators seeking a mapping that is expressive, novel,
and affords them a sense of authorship may not know how to program it up in a
signal processing patch. Tools like Wekinator and MIMIC allow creators to use
supervised machine learning to learn mappings from example input/output
pairings. However, a creator may know a good mapping when they encounter it yet
start with little sense of what the inputs or outputs should be. We call this
an open-ended mapping process. Addressing this need, we introduce the latent
mapping, which leverages the latent space of an unsupervised machine learning
algorithm such as a Variational Autoencoder trained on a corpus of unlabelled
gestural data from the creator. We illustrate it with Sonified Body, a system
mapping full-body movement to sound which we explore in a residency with three
dancers.
