Keypoints and Action Units Jointly Drive Talking Head Generation for Video Conferencing
This paper introduces a high-quality talking head generation method that is jointly driven by keypoints and action units, aiming to strike a balance between low-bandwidth transmission and high-quality generation in video conference scenarios. Existing methods for talking head generation often face limitations: they either require an excessive amount of driving information or struggle with accuracy and quality when adapted to low-bandwidth conditions. To address this, we decompose the talking head generation task into two components: a driving task, focused on information-limited control, and an enhancement task, aimed at achieving high-quality, high-definition output. Our proposed method innovatively incorporates the joint driving of keypoints and action units, improving the accuracy of pose and expression generation while remaining suitable for low-bandwidth environments. Furthermore, we implement a multistep video quality enhancement process, targeting both the entire frame and key regions, while incorporating temporal consistency constraints. By leveraging attention mechanisms, we enhance the realism of the challenging-to-generate mouth regions and mitigate background jitter through background fusion. Finally, a prior-driven super-resolution network is employed to achieve high-quality display. Extensive experiments demonstrate that our method effectively supports low-resolution recording, low-bandwidth transmission, and high-definition display.
Authors
- Yang Wen (ORCID: https://orcid.org/0000-0001-6303-8178)
- Zhiyun Xue (ORCID: https://orcid.org/0000-0003-0644-385X)
- Wuzhen Shi (ORCID: https://orcid.org/0000-0002-6819-0125)
Institutions
- Shenzhen University (CN)
- Shenzhen Technology University (CN)
Publication Details
- Journal
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1109/tcsvt.2025.3560369
- Citations
- 1
- Primary Topic
- Human Motion and Animation
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- National Natural Science Foundation of China
- Shenzhen Science and Technology Innovation Program
- Basic and Applied Basic Research Foundation of Guangdong Province