A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation and Comparison Study

Abstract

Gait recognition has a rapid development in recent years. However, current gait recognition focuses primarily on ideal laboratory scenes, leaving the gait in the wild unexplored. One of the main reasons is the difficulty of collecting in-the-wild gait datasets, which must ensure diversity of both intrinsic and extrinsic human gait factors. To remedy this problem, we propose to construct a large-scale gait dataset with the help of controllable computer simulation. In detail, to diversify the intrinsic factors of gait, we generate numerous characters with diverse attributes and associate them with various types of walking styles. To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout. Then we design an automatic generation toolkit under Unity3D for simulating the walking scenarios and capturing the gait data. As a result, we obtain a dataset simulating towards the in-the-wild scenario, called VersatileGait, which has more than one million silhouette sequences of 10,000 subjects with diverse scenarios. VersatileGait possesses several nice properties, including huge dataset size, diverse pedestrian attributes, complicated camera layout, high-quality annotations, small domain gap with the real one, good scalability for new demands, and no privacy issues. By conducting a series of experiments, we first explore the effects of different factors on gait recognition. We further illustrate the effectiveness of using our dataset to pre-train models, which obtain considerable performance gain on CASIA-B, OU-MVLP, and CASIA-E. Besides, we show the great potential of the fine-grained labels other than the ID label in improving the efficiency and effectiveness of models. Our dataset and its corresponding generation toolkit are available at https://github.com/peterzpy/VersatileGait.