Deep neural networks (DNNs) have repeatedly proven to be successful in modelling complex functions. However, they are known to be data-hungry; as a result, they are often difficult to apply in scenarios where large datasets are not available. Additionally, DNNs are prone to break world governing laws (e.g. physics) in areas like pose estimation. This may be due to the model-free nature of DNNs. Having to learn how the world works from scratch may result in data hungriness and an incorrect world model. My goal is to construct model-based DNNs to obtain greater data efficiency and robustness. Since probabilistic modelling provides a language for describing structural relationships, I intend to utilise it to achieve model-based DNNs. In particular, my goal is to apply these model-based methods to 3D pose estimation for freely moving animals.