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|a 10.1109/TVCG.2024.3488960
|2 doi
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|a eng
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| 100 |
1 |
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|a Wang, Muyu
|e verfasserin
|4 aut
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| 245 |
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|a High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a Date Revised 01.11.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://github.com/muyuWang/HHNeRF
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|a Journal Article
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| 700 |
1 |
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|a Zhao, Sanyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dong, Xingping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shen, Jianbing
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g PP(2024) vom: 31. Okt.
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|u http://dx.doi.org/10.1109/TVCG.2024.3488960
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