  Context. The Vista Variables in the V\'ia L\'actea (VVV) ESO Public Survey is
a variability survey of the Milky Way bulge and an adjacent section of the disk
carried out from 2010 on ESO Visible and Infrared Survey Telescope for
Astronomy (VISTA). VVV will eventually deliver a deep near-IR atlas with
photometry and positions in five passbands (ZYJHK_S) and a catalogue of 1-10
million variable point sources - mostly unknown - which require
classifications. Aims. The main goal of the VVV Templates Project, that we
introduce in this work, is to develop and test the machine-learning algorithms
for the automated classification of the VVV light-curves. As VVV is the first
massive, multi-epoch survey of stellar variability in the near-infrared, the
template light-curves that are required for training the classification
algorithms are not available. In the first paper of the series we describe the
construction of this comprehensive database of infrared stellar variability.
Methods. First we performed a systematic search in the literature and public
data archives, second, we coordinated a worldwide observational campaign, and
third we exploited the VVV variability database itself on (optically)
well-known stars to gather high-quality infrared light-curves of several
hundreds of variable stars. Results. We have now collected a significant (and
still increasing) number of infrared template light-curves. This database will
be used as a training-set for the machine-learning algorithms that will
automatically classify the light-curves produced by VVV. The results of such an
automated classification will be covered in forthcoming papers of the series.
