NeRF-3DTalker: Neural Radiance Field with 3D Prior Aided Audio Disentanglement for Talking Head Synthesis (2502.14178v1)

Abstract: Talking head synthesis is to synthesize a lip-synchronized talking head video using audio. Recently, the capability of NeRF to enhance the realism and texture details of synthesized talking heads has attracted the attention of researchers. However, most current NeRF methods based on audio are exclusively concerned with the rendering of frontal faces. These methods are unable to generate clear talking heads in novel views. Another prevalent challenge in current 3D talking head synthesis is the difficulty in aligning acoustic and visual spaces, which often results in suboptimal lip-syncing of the generated talking heads. To address these issues, we propose Neural Radiance Field with 3D Prior Aided Audio Disentanglement for Talking Head Synthesis (NeRF-3DTalker). Specifically, the proposed method employs 3D prior information to synthesize clear talking heads with free views. Additionally, we propose a 3D Prior Aided Audio Disentanglement module, which is designed to disentangle the audio into two distinct categories: features related to 3D awarded speech movements and features related to speaking style. Moreover, to reposition the generated frames that are distant from the speaker's motion space in the real space, we have devised a local-global Standardized Space. This method normalizes the irregular positions in the generated frames from both global and local semantic perspectives. Through comprehensive qualitative and quantitative experiments, it has been demonstrated that our NeRF-3DTalker outperforms state-of-the-art in synthesizing realistic talking head videos, exhibiting superior image quality and lip synchronization. Project page: https://nerf-3dtalker.github.io/NeRF-3Dtalker.

• The paper introduces a novel framework that integrates 3D priors with audio disentanglement to generate multi-view talking heads with improved lip sync accuracy.
• The methodology leverages a 3D Morphable Model and a Standardized Space to separate speech movements from speaking style, achieving enhanced visual consistency.
• Experimental results demonstrate superior image quality and synchronization compared to state-of-the-art approaches, highlighting its potential in VR and teleconferencing applications.

Overview of NeRF-3DTalker: Neural Radiance Field with 3D Prior Aided Audio Disentanglement for Talking Head Synthesis

In the rapidly evolving field of talking head synthesis, significant advancements have been driven by methods employing Neural Radiance Fields (NeRFs) due to their ability to render high-fidelity images from limited data. The paper "NeRF-3DTalker: Neural Radiance Field with 3D Prior Aided Audio Disentanglement for Talking Head Synthesis," addresses existing challenges within this domain, notably the rendering limitation of frontal views and suboptimal lip-sync alignment between acoustic and visual data spaces.

Methodology

NeRF-3DTalker is introduced to enhance the visual quality and adaptability of synthesized talking heads, especially in non-frontal views, by integrating 3D prior knowledge into the rendering process. This approach effectively utilizes the 3D Morphable Model (3DMM) to extract facial priors, enabling the generation of multi-view consistent facial features by parameterizing the head into shape and appearance categories, including identity, speech-related movements, speaking style, albedo, and illumination.

A significant contribution of this work is the 3D Prior Aided Audio Disentanglement module. This module segregates the audio features into those relevant for speech movements, and those characterizing speaking style. Such a division aims to mitigate the computational burdens on the NeRF and enhance its ability to learn pertinent multimodal features, crucial for precise lip synchronization.

To rectify visual discrepancies observed in frames generated far from the speaker's motion space, a novel Standardized Space is utilized. This method, inspired by codebook concepts, allows for the normalization of frame positions from both global and local semantic perspectives, thereby aligning synthesized frames with the real motion space of the speaker.

Results

Through extensive qualitative and quantitative evaluations, NeRF-3DTalker is demonstrated to outperform state-of-the-art methods in terms of image quality and lip-sync accuracy. The experimental setup contrasts NeRF-3DTalker with non-NeRF approaches such as Wav2Lip and other NeRF-based methods like DFRF and GeneFace. Notably, NeRF-3DTalker achieves superior metric results, indicating its robust synthesis capabilities through the inclusion of 3D priors and enhanced audio-visual alignment.

Implications and Future Directions

NeRF-3DTalker's methodology suggests potential improvements in virtual reality and 3D gaming applications, where realistic and adaptable facial animations are crucial. This paper opens avenues for further integration of more sophisticated disentanglement techniques or enhanced 3D priors for enriched detail and accuracy in dynamic expressions.

Future developments should focus on scaling this method to accommodate diversified real-world applications, such as telepresence in conferencing systems, where synchronized high-fidelity talking animations are vital. Moreover, extending the approach to handle varying head orientations and more complex environments could yield more comprehensive solutions to audio-visual synthesis challenges.

In summary, NeRF-3DTalker sets a promising benchmark in talking head synthesis, leveraging both 3D prior facial features and disentangled audio semantics to enhance the spatial coherence and synchronization accuracy of facial animations.