This paper proposes a path-following framework for finite-dimensional variational inequalities with arbitrary continuous functions and compact convex domains. The approach first approximately reduces a general variational inequality to a smooth variational inequality on a simplex. Its key innovation is to formulate the smooth variational inequality on a simplex on a fiber bundle called the fixed-point bundle. Exploiting this geometric structure, the framework systematically integrates starting point selection, path-following, and singularity avoidance. Without any monotonicity or similar assumptions, the algorithm guarantees global linear convergence to nonsingular solutions. For singular solutions, it maintains global linear reduction up to a prescribed precision, after which convergence becomes sublinear. Numerical experiments on 14400 randomly generated instances with dimensions up to 800 demonstrate robust performance. The algorithm converges in every tested instance, and iteration count grows only mildly with dimension.