# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 1 Introduction  
In recent years, 3D virtual reality technology has become increasingly important in education and entertainment owing to the impact of the spreading COVID-19 disease. Owing to the lack of strong bidirectional interaction in conventional online conference education, various online 3D virtual education systems, such as Framer [1] and Virbela [2], have been developed to enhance immersion and the quality of instruction. They provide users with a virtual representation to interact, create and talk with other people in the virtual 3D world. Some educational research and applications have also been carried out on this virtual platform [3, 4]. In addition, more generic 3D virtual platforms, such as Mootup [5], Vitwayevents [6], Avianet [7], HoloFair [8] and Vfaires [9], have been utilized since the conception and development of the metaverse. They provide solutions for online education, but are equally applicable in the areas of entertainment and social networking. However, the majority of applications presently employ avatars to customize characters. The appearance of avatars is entirely chosen by the user from a range of preset themes. Appearance currently looks more like a preset cartoon than a real avatar with an individual identity. These applications cannot provide a unique personal identity representation for each participant, which is important for participants' immersive interactive experience in educational and entertainment tasks in the virtual world. Therefore, it is necessary to use 3D face reconstruction technology to enhance this experience. 3D face reconstruction technology can obtain a 3D representation of a face from a single image. The low cost of collecting 2D photos makes it easier to implement 3D face reconstruction technologies in intelligent systems [10]. In this technique, face texture is an important representation information. Typically, the acquisition of 3D face information requires the use of stereo vision equipment, such as 3D scanners and depth cameras [11], whereas the gathering of true texture information frequently necessitates expensive scanning [12]. Conversely, one can anticipate the 3D shape and look from 2D images and combines the convenience of 2D facial images with the benefits of 3D facial representations. However, restoring the facial texture from a single 2D image is an ill-posed problem owing to the absence of invisible component information, which results in incorrect appearances in the 3D model. In recent years, several researchers have used conventional neural networks (CNNs) to recover and reconstruct 3D facial representations owing to the high feature extraction capacity of CNNs. However, most studies have focused on 3D facial structure recovery and have neglected the restoration of facial texture information. Furthermore, some studies have restored texture information by regressing the texture coefficients of the 3D morphable model (3 DMM). However, owing to the limited expressive capabilities of the default texture space, the texture is frequently insufficiently realistic or missing for the invisible areas of the face. Therefore, the quality of 3D face texture reconstruction must be further enhanced. A large dataset of human face textures is difficult to obtain and texture restoration from a single image is challenging because of self-occlusion, which results in missing information on the invisible parts of the input image. Therefore, to solve the above-mentioned problems, this study introduces a low-cost approach for generating a human face texture dataset and a panoramic texture translator for 3 DMM-based 3D face reconstruction methods. Texture translator is applicable to 3 DMM-based 3D face reconstruction methods [13; 14; 15; 16; 17] and offers a panoramic texture for a 3D face mesh. Compared with the traditional simple mesh topology, 3 DMM models often comprise a given number of mesh vertices, predefined topology and a predefined texture space, making them easier to deploy or transfer to 3D VR, AR and MR applications [18; 19].