The modeling of interface advancement in a porous medium with two-phase flows becomes a challenge in multi-scale approaches, in many engineering problems such as the prediction of void formation in resin transfert modeling (RTM) processes. In the literature, Blanco et al. in [1] proposed a work on the modeling of steady single-phase incompressible flow in a porous medium. As opposed to conventional approaches which rely on Darcy’s law, they have used the Navier-Stokes model to describe this phenomenon. This was done through a multi-scale approach based on the principle of multi-scale virtual power. Shakoor and Park in [2] completed their work by taking into account the inertial effect. They used the Finite Element (FE) method to implement the proposed model in parallel with a strong coupling between coarse and fine scales. In the case of two-phase flows, single-scale approaches such as Volume of Fluid or Level-Set methods have been extensively developed in the literature but not multi-scale interface tracking methods. In this work, we propose to model two-phase flows in a porous medium, with surface tension at the interface, and track the interface from the fine scale to the coarse scale. The resin/air front progression model, at the pore scale, is the transport equation of the interface. From this equation, and through the development of a multi-scale virtual power method principle, we establish the model of interface progression at the macro scale. The numerical implementation method is the FE method, in the form of parallel FE×FE between the two scales. This approach helps to predict the multi-scale flow front advancement and the behavior of the interface in two-phase fluid flows. [1] P J Blanco, A Clausse, and R A Feijóo. Homogenization of the navier-stokes equations by means of the multi-scale virtual power principle. Computer Methods in Applied Mechanics and Engineering, 315:760–779, 2017. [2] M Shakoor and C H Park. Computational homogenization of unsteady flows with obstacles. International Journal for Numerical Methods in Fluids, 95(4):499–527, 2023.