  Synthesizing realistic data samples is of great value for both academic and
industrial communities. Deep generative models have become an emerging topic in
various research areas like computer vision and signal processing. Affective
computing, a topic of a broad interest in computer vision society, has been no
exception and has benefited from generative models. In fact, affective
computing observed a rapid derivation of generative models during the last two
decades. Applications of such models include but are not limited to emotion
recognition and classification, unimodal emotion synthesis, and cross-modal
emotion synthesis. As a result, we conducted a review of recent advances in
human emotion synthesis by studying available databases, advantages, and
disadvantages of the generative models along with the related training
strategies considering two principal human communication modalities, namely
audio and video. In this context, facial expression synthesis, speech emotion
synthesis, and the audio-visual (cross-modal) emotion synthesis is reviewed
extensively under different application scenarios. Gradually, we discuss open
research problems to push the boundaries of this research area for future
works.
