  Open clusters are key coeval structures that help us understand star
formation, stellar evolution and trace the physical properties of our Galaxy.
In the past years, the isolation of open clusters from the field has been
heavily alleviated by the access to accurate large-scale stellar parallaxes and
proper motions along a determined line of sight. Still, there are limitations
regarding their completeness since large-scale studies rely on optical
wavelengths. Here we extend the open clusters sequences towards fainter
magnitudes complementing the Gaia photometric and astrometric information with
near-infrared data from the VVV survey. We performed a homogeneous analysis on
37 open clusters implementing two coarse-to-fine characterization methods:
extreme deconvolution Gaussian mixture models coupled with an unsupervised
machine learning method on 8-dimensional parameter space. The process allowed
us to separate the clusters from the field at near-infrared wavelengths. We
report an increase of $\sim$47\% new member candidates on average in our sample
(considering only sources with high membership probability p$\geqq$0.9). This
study is the second in a series intended to reveal open cluster near-infrared
sequences homogeneously.
