Listener head generation centers on generating non-verbal behaviors (e.g., smile) of a listener in reference to the information delivered by a speaker. A significant challenge when generating such responses is the non-deterministic nature of fine-grained facial expressions during a conversation, which varies depending on the emotions and attitudes of both the speaker and the listener. To tackle this problem, we propose the Emotional Listener Portrait (ELP), which treats each fine-grained facial motion as a composition of several discrete motion-codewords and explicitly models the probability distribution of the motions under different emotion in conversation. Benefiting from the ``explicit'' and ``discrete'' design, our ELP model can not only automatically generate natural and diverse responses toward a given speaker via sampling from the learned distribution but also generate controllable responses with a predetermined attitude. Under several quantitative metrics, our ELP exhibits significant improvements compared to previous methods.
@article{arxiv.2310.00068,
title = {Emotional Listener Portrait: Neural Listener Head Generation with Emotion},
author = {Luchuan Song and Guojun Yin and Zhenchao Jin and Xiaoyi Dong and Chenliang Xu},
journal= {arXiv preprint arXiv:2310.00068},
year = {2023}
}