Human Head Animation Generation Methods Driven by Multimodality

Peiyu Tsai

doi:10.54254/2755-2721/2026.TJ29059

Applied and Computational EngineeringOpen access

Human Head Animation Generation Methods Driven by Multimodality

Research Article

Open Access

Human Head Animation Generation Methods Driven by Multimodality

Peiyu Tsai ^1*

¹ College of Information Science and Technology, Jinan University, Guangzhou, 511443, China

^*Corresponding author: caipeyu@stu2022.jnu.edu.cn

Published on 5 November 2025

ACE Vol.203

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-515-8

ISBN (Online): 978-1-80590-516-5

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Abstract

With the rapid development of multimodal human-computer interaction, generating high-fidelity, emotionally rich, and naturally coordinated human head animation based on multi-source inputs such as language, images, and text has become a core issue in virtual human research. This article systematically reviews the representative methods in this field in the past five years, and conducts a classified analysis around Transformer-like sequence modeling, the gradual generation mechanism of diffusion models, the NeRF implicit three-dimensional modeling path, and other specialized architectures. Through vertical technological evolution and horizontal performance comparison, the advantages and bottlenecks of various methods in semantic understanding, emotion driving, perspective consistency, and style expression are revealed. It is further pointed out that the fine-grained control of the emotion-driven mechanism, the balance between 3D modeling efficiency and authenticity, and the high degree of customization of personalized appearance and behavioral style will determine the expressive boundaries of the virtual human image. Future research needs to continue to make breakthroughs in cross-modal feature fusion, semantic consistency modeling, and long sequence generation stability, so as to build a virtual human system with human-like interaction capabilities and provide theoretical support and technical reference for application scenarios such as digital human social interaction, education, and entertainment.

Keywords:

multimodal human-computer interaction, human head animation generation, Transformer model, diffusion model, NeRF modeling

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References

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[2]. Chen, Y., Liang, S., Zhou, Z. (2025). HunyuanVideo-Avatar: High-fidelity audio-driven human animation for multiple characters. arXiv preprint arXiv: 2505.20156.

[3]. Huang, Y., Wang, J., Zeng, A. (2023). Dreamwaltz: Make a scene with complex 3D animatable avatars. Advances in Neural Information Processing Systems, 36, 4566–4584.

[4]. Drobyshev, N., Casademunt, A. B., Vougioukas, K. (2024). Emoportraits: Emotion-enhanced multimodal one-shot head avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8498–8507).

[5]. Fei, H., Zhang, H., Wang, B. (2024). Empathyear: An open-source avatar multimodal empathetic chatbot. arXiv preprint arXiv: 2406.15177.

[6]. Wang, H., Weng, Y., Li, Y. (2025). Emotivetalk: Expressive talking head generation through audio information decoupling and emotional video diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 26212–26221).

[7]. Zhen, R., Song, W., He, Q. (2023). Human–computer interaction system: A survey of talking-head generation. Electronics, 12(1), 218.

[8]. Arcelin, B., & Chaverou, N. (2024). Audio2Rig: Artist-oriented deep learning tool for facial and lip sync animation. In ACM SIGGRAPH 2024 Talks (pp. 1–2). ACM.

[9]. Peng, Z., Hu, W., Ma, J. (2025). SyncTalk++: High-fidelity and efficient synchronized talking heads synthesis using Gaussian splatting. arXiv preprint arXiv: 2506.14742.

References

[1]. Liu, C., Lin, Q., Zeng, Z.. (2024). Emoface: Audio-driven emotional 3D face animation. In 2024 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 387–397). IEEE.

[2]. Chen, Y., Liang, S., Zhou, Z. (2025). HunyuanVideo-Avatar: High-fidelity audio-driven human animation for multiple characters. arXiv preprint arXiv: 2505.20156.

[3]. Huang, Y., Wang, J., Zeng, A. (2023). Dreamwaltz: Make a scene with complex 3D animatable avatars. Advances in Neural Information Processing Systems, 36, 4566–4584.

[5]. Fei, H., Zhang, H., Wang, B. (2024). Empathyear: An open-source avatar multimodal empathetic chatbot. arXiv preprint arXiv: 2406.15177.

[7]. Zhen, R., Song, W., He, Q. (2023). Human–computer interaction system: A survey of talking-head generation. Electronics, 12(1), 218.

[8]. Arcelin, B., & Chaverou, N. (2024). Audio2Rig: Artist-oriented deep learning tool for facial and lip sync animation. In ACM SIGGRAPH 2024 Talks (pp. 1–2). ACM.

[9]. Peng, Z., Hu, W., Ma, J. (2025). SyncTalk++: High-fidelity and efficient synchronized talking heads synthesis using Gaussian splatting. arXiv preprint arXiv: 2506.14742.

Cite this article

Tsai,P. (2025). Human Head Animation Generation Methods Driven by Multimodality. Applied and Computational Engineering,203,46-52.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-515-8(Print) / 978-1-80590-516-5(Online)

Editor: Marwan Omar, Guozheng Rao

Conference website: https://www.confspml.org/tianjin.html

Conference date: 21 December 2025

Series: Applied and Computational Engineering

Volume number: Vol.203

ISSN: 2755-2721(Print) / 2755-273X(Online)

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