## 1 Introduction  
According to statistics [1] from the World Health Organization (WHO), about 80% of people suffer from various diseases of the locomotor system, with 45% having flatfoot. In addition, about 25% of women and 15% of men over 30 years of age suffer from foot diseases associated with wearing uncomfortable shoes. Many business processes have moved to a remote format in the last two years due to COVID-19. Nowadays, 3D scanning of human body forms is an emerging field, and the results of such studies can significantly improve the quality of virtual fitting, clothing selection, and the detection of medical diseases in a remote format. This paper deals with the reconstruction of the shape of the human foot. [PERSON] et al. carried out a series of studies [2; 3; 4] involving 3D reconstruction of the entire human body. These studies were unique due to their consideration of data as objects from an imperfect real-world frame; that is, the original data for reconstruction may include noise, glare, highlights, and low photo quality. Two authors' datasets were used in the experiments: a dataset containing synthetic data and a dataset containing real-world data. The synthetic dataset included data generated using Blender software, while the real dataset included human poses recorded using two Intel Realsense devices. In the case of the synthetic dataset, the authors specified strict measurement requirements, including the distance from the cameras to the object and to the ground and the tilt angle. Whengenerating the real-world dataset, participants performed specified actions in front of the camera (e.g., raise an arm, touch nose, turn away from the camera). It was noted that in the case of the real dataset, there were no special requirements for the elimination of glare and illumination in the room. The authors' methodology involved three stages of data processing, including a deep auto-refining adversarial neural network capable of working with real-world depth data of an entire human body. The results were obtained according to EarthMover and Chamfer distance metrics as 0.059 and 0.079, respectively. The processing of noisy real-world data was possible due to the authors' approach of training the adversarial auto-refiner. The addition of adversarial refinement to the network allowed this approach to work with real-world depth-sensor data. The authors also paid attention to the quality of reconstruction depending on the sex of the subjects and the position of the body, considering the possibility of the network being able to restore the distinctly male or female features. The obtained results significantly improved various processes, including virtual fitting, the selection of clothes, and the detection of diseases in a remote format. The purpose of this work was to create a 3D model of the foot suitable for the individual tailoring of shoes. Since the human foot has some anatomical features that require measurements to the nearest millimeter, attention should be paid to the accurate reconstruction of the foot as a separate object of the human body. In the case of reconstruction of the whole body, an error of a few millimeters is acceptable, but when sewing shoes, this can lead to complications of existing foot diseases due to discomfort from shoes being the wrong size. Traditional methods of foot reconstruction, such as the use of stationary laser scanners or plaster castings, cannot be completed with only remote participation from the customer. Orthopedic tailoring of individual shoes is a complicated process due to many different factors, such as individuals having swollen feet, various diseases, and missing parts of the foot. Therefore, in order for custom orders to be completed, companies currently need to operate logistics centers or open branch offices in different cities. This study considers the use of modern computer vision algorithms, photogrammetry, and machine-learning methods to create 3D foot models based on a video stream obtained from a smartphone.