  This paper uses a graphic engine to simulate a large amount of training data
with free annotations. Between synthetic and real data, there is a two-level
domain gap, i.e., content level and appearance level. While the latter has been
widely studied, we focus on reducing the content gap in attributes like
illumination and viewpoint. To reduce the problem complexity, we choose a
smaller and more controllable application, vehicle re-identification (re-ID).
We introduce a large-scale synthetic dataset VehicleX. Created in Unity, it
contains 1,362 vehicles of various 3D models with fully editable attributes. We
propose an attribute descent approach to let VehicleX approximate the
attributes in real-world datasets. Specifically, we manipulate each attribute
in VehicleX, aiming to minimize the discrepancy between VehicleX and real data
in terms of the Fr\'echet Inception Distance (FID). This attribute descent
algorithm allows content domain adaptation (DA) orthogonal to existing
appearance DA methods. We mix the optimized VehicleX data with real-world
vehicle re-ID datasets, and observe consistent improvement. With the augmented
datasets, we report competitive accuracy. We make the dataset, engine and our
codes available at https://github.com/yorkeyao/VehicleX.
