Variations in visual factors such as viewpoint, pose, illumination and
background, are usually viewed as important challenges in person
re-identification (re-ID). In spite of acknowledging these factors to be
influential, quantitative studies on how they affect a re-ID system are still
lacking. To derive insights in this scientific campaign, this paper makes an
early attempt in studying a particular factor, viewpoint. We narrow the
viewpoint problem down to the pedestrian rotation angle to obtain focused
conclusions. In this regard, this paper makes two contributions to the
community. First, we introduce a large-scale synthetic data engine, PersonX.
Composed of hand-crafted 3D person models, the salient characteristic of this
engine is "controllable". That is, we are able to synthesize pedestrians by
setting the visual variables to arbitrary values. Second, on the 3D data
engine, we quantitatively analyze the influence of pedestrian rotation angle on
re-ID accuracy. Comprehensively, the person rotation angles are precisely
customized from 0 to 360, allowing us to investigate its effect on the
training, query, and gallery sets. Extensive experiment helps us have a deeper
understanding of the fundamental problems in person re-ID. Our research also
provides useful insights for dataset building and future practical usage, e.g.,
a person of a side view makes a better query.

本研究通过引入可控的整合 3D 人体模型的大规模数据引擎 PersonX，对行人旋转角度对行人再识别准确性的影响进行了定量分析，从而深入研究了旋转角度等视觉因素对行人再识别系统的影响。