GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation

• URL: http://arxiv.org/abs/2305.00787v1
• Date: Mon, 1 May 2023 12:24:09 GMT
• Title: GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation
• Authors: Zhenhui Ye, Jinzheng He, Ziyue Jiang, Rongjie Huang, Jiawei Huang, Jinglin Liu, Yi Ren, Xiang Yin, Zejun Ma, Zhou Zhao
• Abstract summary: A modern talking face generation method is expected to achieve the goals of generalized audio-lip synchronization, good video quality, and high system efficiency. NeRF has become a popular technique in this field since it could achieve high-fidelity and 3D-consistent talking face generation with a few-minute-long training video. We propose GeneFace++ to handle these challenges by utilizing the rendering pitch contour as an auxiliary feature and introducing a temporal loss in the facial motion prediction process.
• Score: 71.73912454164834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating talking person portraits with arbitrary speech audio is a crucial problem in the field of digital human and metaverse. A modern talking face generation method is expected to achieve the goals of generalized audio-lip synchronization, good video quality, and high system efficiency. Recently, neural radiance field (NeRF) has become a popular rendering technique in this field since it could achieve high-fidelity and 3D-consistent talking face generation with a few-minute-long training video. However, there still exist several challenges for NeRF-based methods: 1) as for the lip synchronization, it is hard to generate a long facial motion sequence of high temporal consistency and audio-lip accuracy; 2) as for the video quality, due to the limited data used to train the renderer, it is vulnerable to out-of-domain input condition and produce bad rendering results occasionally; 3) as for the system efficiency, the slow training and inference speed of the vanilla NeRF severely obstruct its usage in real-world applications. In this paper, we propose GeneFace++ to handle these challenges by 1) utilizing the pitch contour as an auxiliary feature and introducing a temporal loss in the facial motion prediction process; 2) proposing a landmark locally linear embedding method to regulate the outliers in the predicted motion sequence to avoid robustness issues; 3) designing a computationally efficient NeRF-based motion-to-video renderer to achieves fast training and real-time inference. With these settings, GeneFace++ becomes the first NeRF-based method that achieves stable and real-time talking face generation with generalized audio-lip synchronization. Extensive experiments show that our method outperforms state-of-the-art baselines in terms of subjective and objective evaluation. Video samples are available at https://genefaceplusplus.github.io .

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