  Association, aiming to link bounding boxes of the same identity in a video
sequence, is a central component in multi-object tracking (MOT). To train
association modules, e.g., parametric networks, real video data are usually
used. However, annotating person tracks in consecutive video frames is
expensive, and such real data, due to its inflexibility, offer us limited
opportunities to evaluate the system performance w.r.t changing tracking
scenarios. In this paper, we study whether 3D synthetic data can replace
real-world videos for association training. Specifically, we introduce a
large-scale synthetic data engine named MOTX, where the motion characteristics
of cameras and objects are manually configured to be similar to those in
real-world datasets. We show that compared with real data, association
knowledge obtained from synthetic data can achieve very similar performance on
real-world test sets without domain adaption techniques. Our intriguing
observation is credited to two factors. First and foremost, 3D engines can well
simulate motion factors such as camera movement, camera view and object
movement, so that the simulated videos can provide association modules with
effective motion features. Second, experimental results show that the
appearance domain gap hardly harms the learning of association knowledge. In
addition, the strong customization ability of MOTX allows us to quantitatively
assess the impact of motion factors on MOT, which brings new insights to the
community.
