In this PhD thesis the problem of 3D head pose and gaze tracking from minimal user cooperation is addressed. By exploiting characteristics of RGB-D sensors, contributions have been made related to consequent problems of the lack of cooperation: in particular, head pose and inter-person appearance variability; in addition to low resolution handling. The resulting system enabled diverse multimodal applications. In particular, recent work combined multiple RGB-D sensors to detect gazing events in dyadic interactions. The research plan consists of: i) Improving the robustness, accuracy and usability of the head pose and gaze tracking system; ii) To use additional multimodal cues, such as speech and dynamic context, to train and adapt gaze models in an unsupervised manner; iii) To extend the application of 3D gaze estimation to diverse multimodal applications. This includes visual focus of attention tasks involving multiple visual targets, e.g. people in a meeting-like setup.