Abstract
Until now, stellar variability in the near-IR has been a relatively ill-explored research field. In particular, the number of high-quality light curves is very limited and, even worse, many variability classes have not yet been observed in a sufficiently extensive way in the near-IR, so that good light curves are entirely lacking for some such classes. Since VVV is the first ever large survey dedicated to stellar variability in the near-infrared, the first problem we had to face has thus been the construction of a proper statistically significant database of high-quality (i.e., template) near-IR light curves for a significant sample of stars taken to be representative of the different variability classes under study. The main purpose of the VVV Templates Project is thus to build a large database of well-defined, high-quality, near-IR light curves for variable stars of different types, which will form the basis of the VVV automated classification algorithms
