VersatileGait: A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation

TL;DR

VersatileGait is a large-scale synthetic gait dataset created using Unity3D simulation, providing diverse, high-quality gait data for in-the-wild recognition research, addressing data scarcity and domain gap issues.

Contribution

The paper introduces VersatileGait, a comprehensive synthetic gait dataset with over one million sequences, and an automated toolkit for diverse, scalable gait data generation.

Findings

VersatileGait contains over one million silhouette sequences.

The dataset exhibits high diversity in gait attributes and scenarios.

VersatileGait reduces the domain gap with real-world data.

Abstract

Gait recognition has a rapid development in recent years. However, gait recognition in the wild is not well explored yet. An obvious reason could be ascribed to the lack of diverse training data from the perspective of intrinsic and extrinsic 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 empower them with various types of walking styles. To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout. Finally, we design an automated generation toolkit under Unity3D for simulating the walking scenario and capturing the gait data automatically. As a result, we obtain an in-the-wild gait dataset, 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. Based on VersatileGait, we propose series of experiments and applications for both research exploration of gait in the wild and practical applications. Our dataset and its corresponding generation toolkit will be publicly available for further studies.