AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
Abstract
Generating high-fidelity talking head video by fitting with the input audio sequence is a challenging problem that receives considerable attentions recently. In this paper, we address this problem with the aid of neural scene representation networks. Our method is completely different from existing methods that rely on intermediate representations like 2D landmarks or 3D face models to bridge the gap between audio input and video output. Specifically, the feature of input audio signal is directly fed into a conditional implicit function to generate a dynamic neural radiance field, from which a high-fidelity talking-head video corresponding to the audio signal is synthesized using volume rendering. Another advantage of our framework is that not only the head (with hair) region is synthesized as previous methods did, but also the upper body is generated via two individual neural radiance fields. Experimental results demonstrate that our novel framework can (1) produce high-fidelity and natural results, and (2) support free adjustment of audio signals, viewing directions, and background images. Code is available at https://github.com/YudongGuo/AD-NeRF.
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AD-NeRF is not designed for zero-shot generation of unseen identities.
“For example, AD-NeRF [63] is not designed for zero-shot generation of unseen identities”
From Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization (2022) · cites this paper as [63]
Cites
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Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Zhou, H., Liu, Y., Liu, Z., Luo, P., & Wang, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019299 · Cited by 416 · see citing works
Self-citationText-based Editing of Talking-head Video
Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323028 · Cited by 270 · see citing works
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Zhang, W., Cun, X., Wang, X., Zhang, Y., Shen, X., Guo, Y., Shan, Y., & Wang, F. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00836 · Cited by 336 · see citing works
Self-citationDiffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
Stypułkowski, M., Vougioukas, K., He, S., Zięba, M., Petridis, S., & Pantic, M. (2023) · arXiv · doi:10.48550/arxiv.2301.03396 · Cited by 140
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
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Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation
Gan, Y., Yang, Z., Yue, X., Sun, L., & Yang, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02069 · Cited by 72 · see citing works
Deep learning for visual speech analysis: A survey
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Self-citationAV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
Cai, Z., Ghosh, S., Adatia, A. P., Hayat, M., Dhall, A., Gedeon, T., & Stefanov, K. (2024) · Proceedings of the 32nd ACM International Conference on Multimedia · doi:10.1145/3664647.3680795 · Cited by 59 · see citing works
“The variation in the number of subjects and videos in different sets is presented in Table 4 and Figure 4.”
Generalization of Forgery Detection With Meta Deepfake Detection Model
Tran, V.-N., Kwon, S.-G., Lee, S.-H., Le, H.-S., & Kwon, K.-R. (2023) · IEEE Access · doi:10.1109/access.2022.3232290 · Cited by 47 · see citing works
“AD-NeRF [28] proposed an audio-driven talking head technique that renders portraits by directly mapping audio characteristics to dynamic neural radiance fields.”
Background
- Authors
- Guo, Y., Chen, K., Liang, S., Liu, Y.-J., Bao, H., & Zhang, J.
- Affiliations
- University of Science and Technology of China (CN)
- Zhejiang University (CN)
- Tsinghua University (CN)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 5764-5774
- OpenAlex
- W3211147706
- Cited by
- 384
- References
- 5
- Citations by year
- 2026: 53 · 2025: 103 · 2024: 118 · 2023: 75 · 2022: 34 · 2021: 1
- FWCI
- 18.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- talking head synthesis, audio-driven video generation, neural radiance fields, volume rendering, neural scene representation
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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