FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models

核心概念

FaceTalk introduces a novel generative approach for synthesizing high-fidelity 3D motion sequences of talking human heads from input audio signals, utilizing diffusion models in the expression space of neural parametric head models.

要約

• Introduction: FaceTalk proposes a diffusion-based method to synthesize realistic 3D head animations from audio signals.
• Abstract: The method couples speech signals with neural parametric head models to create high-fidelity motion sequences.
• Dataset Creation: Utilizes the Nersemble dataset for paired audio-NPHM expression data creation.
• Method: Implements a transformer-based latent diffusion model for audio-driven head animation synthesis.
• Results: Outperforms existing methods in lip synchronization and visual fidelity, as confirmed by user studies.

統計

Given an input speech signal, we propose a diffusion-based approach to synthesize high-quality and temporally consistent 3D motion sequences of high-fidelity human heads as neural parametric head models. Our method can generate a diverse set of expressions (including wrinkles and eye blinks) and the generated mouth motion is temporally synchronized with the given audio signal.
To the best of our knowledge, this is the first work to propose a generative approach for realistic and high-quality motion synthesis of volumetric human heads, representing a significant advancement in the field of audio-driven 3D animation.
Our approach stands out in its ability to generate plausible motion sequences that consistently achieve superior and visually natural motion, encompassing diverse facial expressions and styles, outperforming existing methods by 75% in perceptual user study evaluation.

引用

"Our experimental results substantiate the effectiveness of FaceTalk, consistently achieving superior and visually natural motion." - Authors

抽出されたキーインサイト

FaceTalk

by Shiv... 場所 arxiv.org 03-19-2024

https://arxiv.org/pdf/2312.08459.pdf

深掘り質問

How can FaceTalk's methodology be applied to other forms of animation beyond facial expressions

FaceTalk's methodology can be applied to other forms of animation beyond facial expressions by adapting the model to different types of parametric models. For example, instead of focusing on facial expressions, the same approach could be used to generate realistic and high-fidelity animations for body movements or even object animations. By modifying the latent space and conditioning signals, FaceTalk could potentially synthesize motion sequences for a wide range of applications such as character animation in movies, video games, or virtual reality environments.

What potential limitations or challenges may arise when implementing FaceTalk in real-time applications

When implementing FaceTalk in real-time applications, several limitations and challenges may arise. One major challenge is the computational complexity of the diffusion-based approach used by FaceTalk. Real-time applications require quick processing times which might be difficult to achieve with multiple denoising steps involved in diffusion models. This could lead to latency issues and hinder real-time performance.
Another limitation is related to data availability and training time. Generating diverse datasets with corresponding NPHM expressions for various audio inputs can be time-consuming and resource-intensive. Additionally, ensuring that the model generalizes well across different identities and speaking styles poses a challenge in real-world scenarios where input variability is high.
Furthermore, integrating FaceTalk into existing systems or software may require significant modifications due to its unique architecture and design choices. Ensuring compatibility with different platforms and frameworks while maintaining performance standards can also be a hurdle when implementing FaceTalk in real-time applications.

How might advancements in facial animation technology impact industries such as entertainment or virtual communication

Advancements in facial animation technology driven by methods like FaceTalk have the potential to revolutionize industries such as entertainment and virtual communication. In entertainment, more realistic facial animations can enhance storytelling capabilities in animated movies or video games by creating lifelike characters with nuanced expressions that resonate better with audiences.
In virtual communication, improved facial animation technology enables more immersive experiences in virtual meetings or online interactions through avatars that accurately reflect users' emotions and gestures. This can lead to enhanced engagement levels during remote collaborations or presentations where non-verbal cues play a crucial role.
Moreover, advancements in facial animation technology can streamline content creation processes by automating complex tasks like lip-syncing or expression generation. This not only saves time but also opens up new possibilities for creators to explore innovative storytelling techniques without being limited by technical constraints.
Overall, advancements in facial animation technology are poised to drive innovation across various industries by enhancing visual quality, user experience, and creative possibilities through realistic digital representations of human faces.