Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

Author:

Li Jiahe1,Zhang Jiawei1,Bai Xiao1,Zhou Jun2,Gu Lin3

Affiliation:

1. Beihang University,School of Computer Science and Engineering, State Key Laboratory of Software Development Environment, Jiangxi Research Institute

2. Griffith University,School of Information and Communication Technology

3. RIKEN AIP

Funder

National Natural Science Foundation of China

Publisher

IEEE

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ER-NeRF++: Efficient region-aware Neural Radiance Fields for high-fidelity talking portrait synthesis;Information Fusion;2024-10

2. A contrastive learning based unsupervised multi-view stereo with multi-stage self-training strategy;Displays;2024-07

3. Editing Audio-Driven Talking Head Based on Audio Information;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. ERF: Generating Hierarchical Editing Audio-Driven Talking Head Using Dynamic Neural Radiance Fields;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. DT-NeRF: Decomposed Triplane-Hash Neural Radiance Fields For High-Fidelity Talking Portrait Synthesis;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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