A critical capability required for the wide adoption of mobile robots into society is the ability to navigate safely around pedestrians. One important component to enable safe navigation is to accurately predict the motion of pedestrians in the scene. The main objective of this research is to develop novel techniques that accurately predict human motion by using past motion and intent as a prior for making the prediction.In this study, we develop neural network architectures that are capable of learning environment-agnostic embeddings that serve as a prior for prediction. We combine these embeddings with contextual information including desired velocity and a probability distribution describing the intent to make predictions. We compare the average displacement error and final displacement error with state-of-the-art published results and show evidence that combining contextual information results in more accurate prediction of future motion.