A Review of Human Emotion Synthesis Based on Generative Technology
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915055864381440 |
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| author | Ma, Fei Li, Yukan Xie, Yifan He, Ying Zhang, Yi Ren, Hongwei Liu, Zhou Yao, Wei Ren, Fuji Yu, Fei Richard Ni, Shiguang |
| author_facet | Ma, Fei Li, Yukan Xie, Yifan He, Ying Zhang, Yi Ren, Hongwei Liu, Zhou Yao, Wei Ren, Fuji Yu, Fei Richard Ni, Shiguang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_07116 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review of Human Emotion Synthesis Based on Generative Technology Ma, Fei Li, Yukan Xie, Yifan He, Ying Zhang, Yi Ren, Hongwei Liu, Zhou Yao, Wei Ren, Fuji Yu, Fei Richard Ni, Shiguang Machine Learning Artificial Intelligence Computation and Language 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. |
| title | A Review of Human Emotion Synthesis Based on Generative Technology |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2412.07116 |