# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 1 Introduction  
The field of 3D body reconstruction is crucial in various industries, such as virtual reality [1], augmented reality [2], and biomedical applications [3]. This process involves reconstructing a detailed 3D model of the human body from a single 2D image [4], a task fraught with challenges due to the ill-posed nature of obtaining three-dimensional data from two-dimensional representations [5]. This complexity arises because multiple 3D points can project identically onto a 2D plane, introducing significant ambiguity in the reconstruction process. Despite these hurdles, advances in computational techniques continue to improve the precision and reliability of 3D body reconstruction [6], broadening its applicability in fields such as medical imaging [7], gaming [8], and robotics [9]. In particular, reconstructing an accurate 3D human form from imperfect and incomplete data requires advanced handling of non-rigid body dynamics and articulated joint movements [10]. Recent breakthroughs in deep learning have enabled more sophisticated end-to-end reconstructions of human shapes [11], including detailed meshes that capture complex articulations. Historically, deep neural networks have faced challenges, such as producing rugged, blurred, or distorted meshes [12]. However, models such as Skinned MultiPerson Linear (SMPL) [13] and its extension, SMPL eXpressive (SMPL-X) [14], have revolutionized this domain. These models offer streamlined representations of 3D humanfigures. When integrated with deep learning techniques, they facilitate the extraction of robust image features and the regression of accurate body shape and pose parameters from standard RGB images. This enhanced methodology pushes the limits of what is possible in 3D human body cloud point reconstruction, promising increasingly accurate and versatile applications [15]. Various technologies are used to estimate human pose [16], including RGB cameras [17], thermal cameras [18], Hyperspectral [19] and IR-Ultra-wideband (UWB) RADAR [20]. Although infrared cameras excel in object detection under low-light conditions, particularly for objects with higher temperatures, they perform poorly with cooler objects. Alternatively, RGB cameras are challenged by low-light environments, although using them in the NIR spectrum can be costly. Single-pixel imaging (SPI) systems present a viable alternative [21], which overcomes many limitations of conventional and thermal cameras in dim settings. SPI systems capture images by measuring the light reflected from an object using a single-pixel detector, even in the infrared spectral bands [22]. Using deep learning, single-pixel imaging (SPI) can reconstruct high-quality images from sparse measurements. It is particularly effective for capturing 3D human body point clouds in challenging scenarios such as night-time surveillance or rescue operations. This capability establishes SPI as a leading method for detailed and accurate pose detection under low-light conditions. Deep learning techniques are highly recommended in such scenarios, and the Vision Transformer (ViT) [23] is emerging as a robust approach to handling low-resolution data. A key advantage of SPI over conventional cameras is its ability to capture data in the NIR spectrum, significantly enhancing 3D point cloud imaging [24]. NIR imaging performs exceptionally well under low light conditions, providing superior clarity for object detection and tracking, an essential feature for advanced rescue applications [25]. When combined with time-of-flight (TOF) sensors, SPI technology produces highly accurate 2D and 3D environmental images [26]. This integration enhances the depth of the data, offering detailed insight into the spatial arrangement and motion of objects within a scene. Furthermore, SPI is not limited to low-light scenarios; it remains highly effective in challenging environments, such as those with dust or fog, where traditional cameras struggle, ensuring consistent and reliable 3D data acquisition under various conditions.