# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 2 Related Works  
### Research on Body Shape Inference  
Based on the type of data they use, body shape inference methods can be divided into four categories: three-dimensional (3D) sequences, single 3D scans, single RGB images, and multi-view RGB images. Methodologies that use depth cameras, 3D scanners, etc., to infer the shape of the naked body use 3D sequences and single 3D images, whereas the remaining types use data acquired using regular cameras.  
Three-dimensional image data are collected largely using Microsoft's Kinect cameras. To infer the disrobed body and the corresponding parametric model, the collected data are translated into a 3D sequence [34, 35]. However, these technologies are hampered by the high cost of depth cameras used to capture 3D images. They also necessitate creating algorithms that transform 3D images into a series and algorithms that ideally match the parameters of the parametric model with the resulting 3D image sequence, both of which tend to be complicated. Even if the 3D image sequence is converted, and parametric model matching is utilized to infer the unclothed body, matching the ideal parametric model requires a long time. Furthermore, to acquire 3D images, participants must wear skin-tight clothing, which can lead to difficulties such as invasion of privacy and refusal to participate.  
To address these problems, efforts have been made to develop methods of deducing the body shape of an unclothed person from a single 3D image [26; 27]. For example, some of these methods infer the body shape from a single 3D image and weight based on the sizes of the clothes. However, there are problems with these techniques, such as setting weights for various clothing sizes and the accuracy of the body shape generated based on the selection of the range of clothing, and the drawback of utilizing a parametric model. As indicated earlier, this model requires a long computation time. Even recent studies focusing on 3D optical imagery have aimed to improve accuracy by applying deep 3D convolutional networks and non-linear regression, acknowledging the limitations of previous linear algorithms [36].  
To lower the cost of 3D image acquisition, RGB images have recently been utilized to infer unclothed body shapes [28; 29; 30; 31]. Image processing algorithms, deep learning methodologies, and other techniques have been used in these research studies to extract body silhouettes and body joint points from photos and match them with parametric models to construct body shapes. However, there are drawbacks, such as limited postures for acquiring body silhouettes and joint points from test subjects, and the low accuracy of body shapes generated by image processing algorithms and deep learning approaches. To overcome these challenges, recent research has introduced more sophisticated approaches. For instance, SHAPY tackles the scarcity of paired image and 3D scan data by training a network with easily obtainable anthropometric measurements and linguistic shape attributes, thereby improving 3D body shape regression from a single image [37]. Similarly, the GenHMR framework reformulates the problem by explicitly modeling uncertainties in the 2D-to-3D mapping, which improves the estimation of 3D pose and shape, especially in cases with occlusions [38].