# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 1 Introduction  
Data and information collected from the human body are becoming increasingly rich and varied because of recent technological advancements in data collection and storage. Research studies have been conducted in various fields, including healthcare, virtual environments, and the clothing industry, to collect data on the human body using the Internet of Things (IoT) and to measure the human body using artificial intelligence (AI) technologies such as machine learning and deep learning [1, 2, 3, 4]. Therein, the human body is measured to extract body data and information such as height, weight, body shape, and posture, which are used primarily for medical, safety, and aesthetic purposes [5, 6, 7, 8]. For example, in the healthcare industry, the analysis of such information as height, weight, and body shape through body estimation is used in research on obesity management [9, 10]. In sectors such as personal healthcare and virtual clothing try-on, photos and videos of the user's body are used to provide personalized services. In recent years, research on the human body has also been conducted vigorously in industries associated with the Fourth Industrial Revolution. In autonomous transportation, pedestrians are detected using cameras installed on autonomous vehicles or CCTV images installed in road infrastructure, and pedestrian intentions (i.e., whether the pedestrian stops or crosses the road) are analyzed [11]. The analyzed pedestrian intentions are applied to route planning and secure operation of autonomous vehicles [12]. In virtual reality, body shape information is extracted from human images and used to create the user's avatar on the metaverse [13]. Height, weight, morphology, and posture are frequently obtained from the human body for use in studies on the human body. Body shape data can be gathered using bothdirect and indirect methods. Direct measurement is performed via the manual collection of information, such as height, weight, and torso circumference, using a tape measure, scale, etc. [14]. On the other hand, indirect measurements collect such information as posture and body shape through the use of computational technology [15; 16]. Early research on the human body relied predominantly on direct measurement techniques to gather body data [17; 18; 19]. However, using direct measurement methods to acquire data can be time-consuming and, depending on the measurement tool, inaccurate. Therefore, as computational technology advanced, research on the human body has relied primarily on indirect measurement techniques [20; 21; 22]. Examples include image processing algorithms and deep learning to capture data from images of the human body [15; 16]. However, indirect measurement methods can be extremely inaccurate because the data collected are affected by the clothes worn on the body, necessitating that subjects wear minimal or skin-tight clothing [23; 24]. These requirements have led to problems such as invasion of privacy and resistance to measurement. Accordingly, this study aimed to design a body shape inference method that performs well even when the subject wears loose or occlusive garments. Our goal was to develop a robust and practical solution that enables indirect measurement while addressing privacy and usability concerns. Various studies have been conducted to infer body regions covered by clothes and determine body shapes. However, previous research studies had limitations, such as low accuracies of body shape measurement owing to the shape of clothing [25; 26; 27] and high computation times for the parametric generation model [28; 29; 30; 31]. Recently, some studies have attempted to overcome these limitations by addressing the problem of 3D human body reconstruction under clothing. For example, [32] proposed a method that generates realistic clothing deformation based on SMPL body meshes and 3D supervision.