  This article aims to use graphic engines to simulate a large number of
training data that have free annotations and possibly strongly resemble to
real-world data. Between synthetic and real, a two-level domain gap exists,
involving content level and appearance level. While the latter is concerned
with appearance style, the former problem arises from a different mechanism,
i.e, content mismatch in attributes such as camera viewpoint, object placement
and lighting conditions. In contrast to the widely-studied appearance-level
gap, the content-level discrepancy has not been broadly studied. To address the
content-level misalignment, we propose an attribute descent approach that
automatically optimizes engine attributes to enable synthetic data to
approximate real-world data. We verify our method on object-centric tasks,
wherein an object takes up a major portion of an image. In these tasks, the
search space is relatively small, and the optimization of each attribute yields
sufficiently obvious supervision signals. We collect a new synthetic asset
VehicleX, and reformat and reuse existing the synthetic assets ObjectX and
PersonX. Extensive experiments on image classification and object
re-identification confirm that adapted synthetic data can be effectively used
in three scenarios: training with synthetic data only, training data
augmentation and numerically understanding dataset content.
