3D motion-capture is essential for research in biomechanics and biomedical engineering. It can provide insight into performance and injury prevention in sport, diagnosis of illnesses and disorders as well as help biologists to study animals and help engineers to design bio-inspired robots. Researchers studying animal locomotion often study humans and dogs due to the difficulty associated with studying other animals; however, the cheetah (Acinonyx jubatus) is a particularly compelling animal to study due to its various cursorial adaptions. In recent years, there have been significant improvements in computer-based pattern recognition in deep learning, specifically convolutional neural networks (CNNs). This project will explore the use of computer vision techniques including CNNs, extended Kalman filters (EKFs), non-linear optimisation and sparse bundle adjustment (SBA) to remove the need for markers to be used in recovering a subject's location from video footage. The result of the project will be the development of a markerless 3D motion-capture system. The thesis discusses the theory behind and describes the development of tools for video synchronisation and processing, camera calibration, pose estimation, robust 3D reconstruction and 3D pose visualisation. Results are shown for motion capture performed on video footage of cheetahs. Visualisations and 3D motion data of these agile animals are also shown. The system developed is an enabling tool in the study of biomechanics and biomedical research.