  Affective computing is an emerging interdisciplinary field where
computational systems are developed to analyze, recognize, and influence the
affective states of a human. It can generally be divided into two subproblems:
affective recognition and affective generation. Affective recognition has been
extensively reviewed multiple times in the past decade. Affective generation,
however, lacks a critical review. Therefore, we propose to provide a
comprehensive review of affective generation models, as models are most
commonly leveraged to affect others' emotional states. Affective computing has
gained momentum in various fields and applications, thanks to the leap of
machine learning, especially deep learning since 2015. With critical models
introduced, this work is believed to benefit future research on affective
generation. We conclude this work with a brief discussion on existing
challenges.
