Person re-identification is a challenging computer vision task where one wants to match each probe pedestrian to the corresponding images in the gallery set. Pose, viewpoint and illumination variations have been well-known issues. Despite this, recent developments have shown positive results when models are trained and tested on the same dataset. However, different datasets present unrelatable characteristics, to the point that they define distinct domains. So far, achieving a good performance on cross-domain approaches has been proven to be much more demanding than training standard supervised methods. Recent models that bridge the gap across domains have drawn significant attention since, from a practical perspective, annotating new data is error-prone and time-consuming, whereas having unlabeled images is much less expensive.