The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index.
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
Open accessAbstract
Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without further finetuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking head videos for generalized novel identities. For more video results, please refer to https://sstzal.github.io/DiffTalk/.
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In the citing paper
“We investigated several face reenactment strategies including EAMM [26], AVFR-GAN [2], DiffTalk [54], ADNeRF [20] and ATVGnet [10] and concluded that these methods are not well suited for zero-shot lip-synced generation of unseen speakers.”
From AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset (2024) · cites this paper as [54]
In the citing paper
“pioneer talking-head generation works typically utilize biometric signals such as facial keypoints [22, 28, 30, 45], or 3D priors [11, 13, 16, 23, 32, 43, 44] as intermediate motion representation to animate the reference face while ensuring lip synchronization.”
From MoCha: Towards Movie-Grade Talking Character Synthesis (2025) · cites this paper as [30]
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Multimodal Image Synthesis and Editing: The Generative AI Era
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Metadata
- Authors
- Shen, S., Zhao, W., Meng, Z., Li, W., Zhu, Z., Zhou, J., & Lu, J.
- Affiliations
- Tsinghua University (CN)
- PhiGent Robotics
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 1982-1991
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075487
- Cited by
- 133
- References
- 58
- Citations by year
- 2026: 25 · 2025: 56 · 2024: 43 · 2023: 9
- FWCI
- 14.25
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2017YFA0700802)
- National Natural Science Foundation of China (62125603)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- talking head synthesis, audio-driven video generation, latent diffusion models, facial landmarks, audio-video synchronization
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