  With the motivation of practical gait recognition applications, we propose to
automatically create a large-scale synthetic gait dataset (called
VersatileGait) by a game engine, which consists of around one million
silhouette sequences of 11,000 subjects with fine-grained attributes in various
complicated scenarios. Compared with existing real gait datasets with limited
samples and simple scenarios, the proposed VersatileGait dataset possesses
several nice properties, including huge dataset size, high sample diversity,
high-quality annotations, multi-pitch angles, small domain gap with the real
one, etc. Furthermore, we investigate the effectiveness of our dataset (e.g.,
domain transfer after pretraining). Then, we use the fine-grained attributes
from VersatileGait to promote gait recognition in both accuracy and speed, and
meanwhile justify the gait recognition performance under multi-pitch angle
settings. Additionally, we explore a variety of potential applications for
research.Extensive experiments demonstrate the value and effective-ness of the
proposed VersatileGait in gait recognition along with its associated
applications. We will release both VersatileGait and its corresponding data
generation toolkit for further studies.
