Contactless measurement of changes in blood volume by exploiting the color fluctuations in the face is a technique commonly referred to as remote photoplethysmography (rPPG). Recent developments show promising results for heart rate estimation from low-cost cameras, making applications in remote healthcare possible. Remote PPG applications in at home diagnostics focus predominantly on heart rate estimation, while other features of the blood volume pulse can provide valuable information as well, but have scarcely been studied using rPPG. In this work, we aim to lay a foundation for rPPG feature prediction. We study pulse wave prediction using a variety of input representations, model architectures and datasets to thereby investigate which combined approaches are the most promising. Our results show which input representation is most suitable based on the feature of interest and demonstrate the ability to predict pulse waveform features using rPPG. These results take the first steps towards including the prediction of a wide range of waveform properties to make more remote health monitoring possible.