NeRF-AD: Neural Radiance Field with Attention-based Disentanglement for Talking Face Synthesis
This work addresses a specific challenge in multimedia for generating realistic talking faces, representing an incremental improvement over existing NeRF-based methods.
The paper tackled the problem of inaccurate lip shapes in audio-driven talking face synthesis by proposing NeRF-AD, which uses attention-based disentanglement and audio feature fusion, resulting in outperforming state-of-the-art methods in realism and lip synchronization.
Talking face synthesis driven by audio is one of the current research hotspots in the fields of multidimensional signal processing and multimedia. Neural Radiance Field (NeRF) has recently been brought to this research field in order to enhance the realism and 3D effect of the generated faces. However, most existing NeRF-based methods either burden NeRF with complex learning tasks while lacking methods for supervised multimodal feature fusion, or cannot precisely map audio to the facial region related to speech movements. These reasons ultimately result in existing methods generating inaccurate lip shapes. This paper moves a portion of NeRF learning tasks ahead and proposes a talking face synthesis method via NeRF with attention-based disentanglement (NeRF-AD). In particular, an Attention-based Disentanglement module is introduced to disentangle the face into Audio-face and Identity-face using speech-related facial action unit (AU) information. To precisely regulate how audio affects the talking face, we only fuse the Audio-face with audio feature. In addition, AU information is also utilized to supervise the fusion of these two modalities. Extensive qualitative and quantitative experiments demonstrate that our NeRF-AD outperforms state-of-the-art methods in generating realistic talking face videos, including image quality and lip synchronization. To view video results, please refer to https://xiaoxingliu02.github.io/NeRF-AD.