To support first-person view trajectory prediction research, we present T2FPV, a method for constructing high-fidelity first-person view datasets given a real-world, top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian dataset to generate the egocentric visual data of all interacting pedestrians. We evaluate existing trajectory prediction approaches under varying levels of realistic perception---displacement errors suffer a 356% increase compared to the top-down, perfect information setting.