# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 1 Introduction  
Human gait is one of the most complex phenomena combining efforts of the neural, muscle, and skeletal systems, with mutual interaction from the external environment. It contains abundant information reflecting some unique features of a person, and therefore has great potential in various applications, especially human recognition. The discrimination of human gait has been intensively investigated in the research of biomechanics, physical medicine, and psychological studies [1]. Compared with other biometric identification methods (such as fingerprints, face, or iris biometric modalities), vision-based gait recognition does not require the cooperation or even awareness of the individual under observation and therefore has been attracting increasing attention [2]. However, accurate recognition of human gait is still challenging due to (1) the inconspicuous **inter-class** differences between different people such as walking style and health condition; (2) the significant **intra-class** variations from the same person in dynamically-changing scenarios such as different walking speeds, viewpoints, clothing, and belongings; and (3) **complex surveillance environments** such as cluttered environments, illumination changes, partial occlusions, and crowds of people. Recently, the popularity of deep neural networks stimulated its usage in gait recognition [3; 4; 5]. Adequate gait data are the prerequisite of many gait recognition algorithms, especially for mainstream supervised methods, and the size of the gait dataset should be sufficiently large in order to reflect the variety of factors and their combinations. Existing works on gait dataset construction are still far from this demanding goal. Takingthe representative OU-ISIR Gait Database [6] as an example, although declared to be the largest dataset in the world (including more than 60,000 subjects), data were collected under a fixed viewpoint and environment. A potential solution for these problems is using highly realistic synthetic data generated from virtual worlds [7; 8; 9]. We can automatically annotate synthetic data for training/validation and conveniently collect samples in extreme or challenging circumstances (such as occlusion and poor illumination). Even more important, synthetic data allow us to pre-train the surveillance system so that it can be used immediately without data collection and training. However, it is non-trivial to synthesize data for gait analysis. The major challenge is that we need to precisely control the variation of synthesized gait style to balance inter-subject and intra-subject differences. In this paper, we present a framework to explore the gait space from various aspects (including camera, illumination, clothes, motion style, etc.). Our method has two major advantages: (1) our method can generate an unlimited number of gait data with arbitrary combinations of influential factors; (2) we can provide accurate metrics for the purpose of gait analysis, including gait energy image, inertial data, ground force, etc. Figure 1 shows the overview of the proposed framework. This work is, to the best of our knowledge, the first one to simultaneously consider the variations of both motion style and character appearance. In particular, we emphasize the synthesis of personalized yet variant gait sequences, which is rarely discussed in existing motion synthesis and editing works. The technical contributions of this paper include:  
* We propose a complete framework to explore gait data generation for the purpose of synthesizing unlimited, realistic, and diverse motion data. These data could serve as the training database for learning-based methods in gait recognition, etc. * We develop a semantic motion style exploration method that controls the motion style via a deep neural network and interpolates motion sequences of different lengths. * We introduce an appearance parameterization model capable of generating virtual characters with different weight, height, girth, clothing, etc.