Synthesizing 3D Gait Data with Personalized Walking Style and Appearance

• Published in: Applied sciences 2023-02, Vol.13 (4), p.2084
• Main Authors: Cheng, Yao, Zhang, Guichao, Huang, Sifei, Wang, Zexi, Cheng, Xuan, Lin, Juncong
• Format: Article
• Language: English
• Subjects: 3-D graphics; 3D gait data; Accelerometers; Algorithms; Biometric identification; Biometrics; Biometry; Data collection; Datasets; Deep learning; Gait; Gait recognition; gait synthesis; human motion data; Methods; neural network; Neural networks; Surveillance; Three dimensional motion; Walking
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042084

Extracting gait biometrics from videos has been receiving rocketing attention given its applications, such as person re-identification. Although deep learning arises as a promising solution to improve the accuracy of most gait recognition algorithms, the lack of enough training data becomes a bottleneck. One of the solutions to address data deficiency is to generate synthetic data. However, gait data synthesis is particularly challenging as the inter-subject and intra-subject variations of walking style need to be carefully balanced. In this paper, we propose a complete 3D framework to synthesize unlimited, realistic, and diverse motion data. In addition to walking speed and lighting conditions, we emphasize two key factors: 3D gait motion style and character appearance. Benefiting from its 3D nature, our system can provide various gait-related data, such as accelerometer data and depth map, not limited to silhouettes. We conducted various experiments using the off-the-shelf gait recognition algorithm and draw the following conclusions: (1) the real-to-virtual gap can be closed when adding a small portion of real-world data to a synthetically trained recognizer; (2) the amount of real training data needed to train competitive gait recognition systems can be reduced significantly; (3) the rich variations in gait data are helpful for investigating algorithm performance under different conditions. The synthetic data generator, as well as all experiments, will be made publicly available.