Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation (2201.07786v1)

Abstract: Animating high-fidelity video portrait with speech audio is crucial for virtual reality and digital entertainment. While most previous studies rely on accurate explicit structural information, recent works explore the implicit scene representation of Neural Radiance Fields (NeRF) for realistic generation. In order to capture the inconsistent motions as well as the semantic difference between human head and torso, some work models them via two individual sets of NeRF, leading to unnatural results. In this work, we propose Semantic-aware Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven portraits using one unified set of NeRF. The proposed model can handle the detailed local facial semantics and the global head-torso relationship through two semantic-aware modules. Specifically, we first propose a Semantic-Aware Dynamic Ray Sampling module with an additional parsing branch that facilitates audio-driven volume rendering. Moreover, to enable portrait rendering in one unified neural radiance field, a Torso Deformation module is designed to stabilize the large-scale non-rigid torso motions. Extensive evaluations demonstrate that our proposed approach renders more realistic video portraits compared to previous methods. Project page: https://alvinliu0.github.io/projects/SSP-NeRF

• The paper introduces SSP-NeRF, a unified neural framework that integrates semantic guidance across facial and torso regions for synchronized audio-driven video synthesis.
• It employs dynamic ray sampling to focus on crucial facial regions, significantly enhancing lip-sync accuracy and overall visual clarity.
• A torso deformation module stabilizes non-rigid motions, reducing artifacts and outperforming state-of-the-art methods in key quality metrics.

Analysis of "Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation"

The paper "Semantic-Aware Implicit Neural Audio-Driven Video Portrait Generation" introduces an innovative approach to generating high-fidelity video portraits driven by speech audio. This work addresses significant challenges in the field of virtual reality and digital entertainment by leveraging Neural Radiance Fields (NeRF) for enhanced realism in video portrait synthesis.

Key Contributions

The authors propose the Semantic-aware Speaking Portrait NeRF (SSP-NeRF), a framework designed to tackle the intrinsic connectivity and differences between facial and torso movements. Unlike previous methods that model head and torso with separate NeRF instances, which often result in unnatural rendering, SSP-NeRF employs unified NeRF powered by semantic-aware modules.

1. Semantic-Aware Dynamic Ray Sampling: SSP-NeRF introduces a Semantic-Aware Dynamic Ray Sampling mechanism. This module incorporates a parsing branch that effectively handles facial semantics, enhancing audio-driven volume rendering by dynamically adjusting ray sampling. This innovation ensures the system pays focused attention to critical regions like lips and teeth for better visual fidelity.
2. Torso Deformation Module: The paper also presents a Torso Deformation module, which uniquely stabilizes non-rigid torso motions by predicting and accommodating the displacement of 3D points. This design addresses the interconnected yet distinct motion patterns of the head and torso, ensuring synchronized and coherent generation of video portraits.

Methodology Overview

The methodology is centered around facilitating detailed local semantic understanding and robust global head-torso relationships within a single NeRF framework. SSP-NeRF does this by:

• Implementing a novel dynamic ray sampling strategy, which allocates computational resources according to the semantic difficulty encountered during parsing, significantly enhancing the image resolution in semantically complex areas.
• Leveraging a parsing network that conditions the NeRF on semantic understanding, enabling the model to be predictively aware of facial semantics and improve lip synchronization with audio inputs.
• Employing a deformation-based modeling of torso movement, the system braces against the instability found in static approaches by dynamically adjusting spatial coordinates in response to changes in head orientation and pose.

Experimental Results

The experiments demonstrate SSP-NeRF's superiority over existing state-of-the-art methods across multiple datasets, with particular improvement in metrics such as PSNR, SSIM, and lip-sync accuracy. The inclusion of semantic guidance not only enhances the realism of facial expressions synchronized with audio but also ensures the overall consistency of head-torso motion during portrait generation.

SSP-NeRF achieves efficient processing and resource allocation through its novel ray sampling approach, evidenced by an accelerated training process and reduced model size.

Broader Implications and Future Directions

While SSP-NeRF sets a new benchmark in semantic-aware neural rendering, the computational demands of high-resolution image synthesis pose a challenge. Additionally, the potential misuse of this technology for generating synthetic portraits necessitates careful ethical consideration. Future work could improve rendering speeds, enhance the model's ability to handle diverse linguistic inputs, and contribute towards developing robust detection mechanisms against synthetic media misuse.

Conclusion

The proposition of SSP-NeRF marks a significant advancement in audio-driven video portrait generation. By embedding semantic awareness and dynamic sampling into the rendering process, this work successfully overcomes key challenges in aligning audio with natural and coherent visual dynamics, facilitating practical applications in digital media and virtual environments while also prompting further research exploration to enhance efficiency and ethical use of neural rendering technologies.