In human pose estimation, a comprehensive evaluation of state-of-the-art frameworks is necessary to advance both research and practical applications. This paper presents a thorough review of state-of-the-art 2D and 3D human pose estimation frameworks, analyzing 118 papers and four GitHub repositories, with a focus on frameworks made since 2019. The following frameworks are chosen based on predefined inclusion criteria: AlphaPose, Detectron2, MediaPipe, MeTRAbs, MHFormer, MMPose, MoveNet, OpenPifPaf, OpenPifPaf-vita, OpenPose, PoseFormerV2, rtmlib, StridedTransformer-Pose3D, ultralytics (YOLOv8), ViTPose, and YOLOv7. This paper evaluates these 16 frameworks on an existing, unpublished dataset consisting of exercise videos recorded with a monocular RGB camera and synchronized gold-standard motion capture data. The dataset includes videos of nine individuals performing eight exercises, recorded from two camera views with different planar angles. The analysis evaluates joint angle performance of the frameworks using weighted mean absolute error and weighted intraclass correlation coefficient as quantitative metrics. MeTRAbs emerged as the best overall framework, while AlphaPose, rtmlib, and YOLOv7 were the top 2D performers.

The COVID-19 pandemic has challenged our day-today life, businesses, and educational institutions by changing the way we interact with each other in a very short space of time. In the context of teaching, lecturers had to rapidly develop concepts and teaching materials that enable distributed virtual and safe learning experiences. In this paper, we describe the challenges of reorganizing a two-week programming course that we have taught for more than ten years in a classroom setting using face-to-face communication. The course teaches students the basics of developing a software system using the Swift programming language and is a prerequisite for a single semester capstone course. We show how we reworked the course into a distributed format using online sessions and real-time feedback for about 80 students. We describe the remote supervision approaches we used to support students and the process of rethinking the course infrastructure by enabling remote access as well as by offering a semi-automated merge management and code review system. Based on this experience, we provide instructors with insights on how to set up and conduct a distributed software engineering course when face-to-face teaching is impossible.