  Nowadays, real data in person re-identification (ReID) task is facing privacy
issues, e.g., the banned dataset DukeMTMC-ReID. Thus it becomes much harder to
collect real data for ReID task. Meanwhile, the labor cost of labeling ReID
data is still very high and further hinders the development of the ReID
research. Therefore, many methods turn to generate synthetic images for ReID
algorithms as alternatives instead of real images. However, there is an
inevitable domain gap between synthetic and real images. In previous methods,
the generation process is based on virtual scenes, and their synthetic training
data can not be changed according to different target real scenes
automatically. To handle this problem, we propose a novel Target-Aware
Generation pipeline to produce synthetic person images, called TAGPerson.
Specifically, it involves a parameterized rendering method, where the
parameters are controllable and can be adjusted according to target scenes. In
TAGPerson, we extract information from target scenes and use them to control
our parameterized rendering process to generate target-aware synthetic images,
which would hold a smaller gap to the real images in the target domain. In our
experiments, our target-aware synthetic images can achieve a much higher
performance than the generalized synthetic images on MSMT17, i.e. 47.5% vs.
40.9% for rank-1 accuracy. We will release this toolkit\footnote{\noindent Code
is available at
\href{https://github.com/tagperson/tagperson-blender}{https://github.com/tagperson/tagperson-blender}}
for the ReID community to generate synthetic images at any desired taste.
