A Review of Human Emotion Synthesis Based on Generative Technology

作     者：Ma, Fei Li, Yukan Xie, Yifan He, Ying Zhang, Yi Ren, Hongwei Liu, Zhou Yao, Wei Ren, Fuji Yu, Fei Richard Ni, Shiguang 

作者机构： Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China  Guangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Information Technology Carleton University Canada 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Generative adversarial networks 

摘      要：Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, and Sequence-to-Sequence Models, have significantly contributed to the development of this field. However, there is a notable lack of comprehensive reviews in this field. To address this problem, this paper aims to address this gap by providing a thorough and systematic overview of recent advancements in human emotion synthesis based on generative models. Specifically, this review will first present the review methodology, the emotion models involved, the mathematical principles of generative models, and the datasets used. Then, the review covers the application of different generative models to emotion synthesis based on a variety of modalities, including facial images, speech, and text. It also examines mainstream evaluation metrics. Additionally, the review presents some major findings and suggests future research directions, providing a comprehensive understanding of the role of generative technology in the nuanced domain of emotion synthesis. Copyright © 2024, The Authors. All rights reserved.