Given the identification with travel demand and its relevance for transportation and urban planning, the estimation of trip flows between areas is a fundamental metric for human mobility. Previous models focus on flow intensity, disregarding the information provided by the local mobility orientation. A field-theoretic approach can overcome this issue and handle both intensity and direction at once. Here we propose a general vector-field representation starting from individuals’ trajectories valid for any type of mobility. We also show with simplified models how individuals’ choices determine the mesoscopic properties of the mobility field. Distance optimization in long displacements and random-like local exploration are necessary to reproduce empirical field features observed in Chinese logistic data and in New York City Foursquare check-ins. Our framework is able to capture hidden symmetries in mesoscopic urban mobility and opens the doors to the use of field theory in a wide spectrum of applications.