arXiv:2503.09852v1 Announce Type: new
Abstract: Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style modeling and do not adequately consider style biases across varying speech conditions, which reduce the accuracy of style modeling and hamper the adaptation capability to unseen speakers. To address this, we propose a novel framework, StyleSpeaker, which explicitly extracts speaking styles based on speaker characteristics while accounting for style biases caused by different speeches. Specifically, we utilize a style encoder to capture speakers’ styles from facial motions and enhance them according to motion preferences elicited by varying speech conditions. The enhanced styles are then integrated into the coarse motion features via a style infusion module, which employs a set of style primitives to learn fine-grained style representation. Throughout training, we maintain this set of style primitives to comprehensively model the entire style space. Hence, StyleSpeaker possesses robust style modeling capability for seen speakers and can rapidly adapt to unseen speakers without fine-tuning. Additionally, we design a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. Extensive qualitative and quantitative experiments on three public datasets demonstrate that our method outperforms existing state-of-the-art approaches.
Expert Commentary: Speech-driven 3D Facial Animation and the Multi-disciplinary Nature of the Concepts
The content discussed in this article revolves around the challenging task of speech-driven 3D facial animation. This topic is inherently multi-disciplinary, combining elements from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Facial animation is a crucial component of many multimedia systems, including virtual reality applications and animated movies. To create realistic and expressive facial animations, it is important to accurately model the intricate details of lip motion and facial expressions. However, existing approaches often struggle to capture the fine-grained nuances of different speaking styles and lack the ability to adapt to unseen speakers.
The proposed framework, StyleSpeaker, addresses these limitations by explicitly extracting speaking styles based on speaker characteristics while considering the style biases caused by different speeches. By utilizing a style encoder, the framework captures speakers’ styles and enhances them based on motion preferences elicited by varying speech conditions. This integration of styles into the coarse motion features is achieved via a style infusion module that utilizes a set of style primitives to learn fine-grained style representation. The framework also maintains this set of style primitives throughout training to comprehensively model the entire style space.
In addition to style modeling, the framework introduces a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. These additional losses contribute to the overall accuracy of the animation and enhance its realism.
The experiments conducted on three public datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of both qualitative and quantitative measures. The combination of style modeling, motion-speech synchronization, and the adaptability to unseen speakers makes StyleSpeaker a promising framework for speech-driven 3D facial animation.
From a broader perspective, this research showcases the interconnectedness of different domains within multimedia information systems. The concepts of 3D facial animation, style modeling, and motion-speech synchronization are essential not only in the context of multimedia applications but also in fields like virtual reality, augmented reality, and artificial reality. By improving the realism and expressiveness of facial animations, this research contributes to the development of immersive experiences and realistic virtual environments.
Key takeaways:
- The content focuses on speech-driven 3D facial animation and proposes a novel framework called StyleSpeaker.
- StyleSpeaker explicitly extracts speaking styles based on speaker characteristics and accounts for style biases caused by different speeches.
- The framework enhances styles according to motion preferences elicited by varying speech conditions, integrating them into the coarse motion features.
- StyleSpeaker possesses robust style modeling capability and can rapidly adapt to unseen speakers without the need for fine-tuning.
- The framework introduces trend loss and local contrastive loss to improve motion-speech synchronization.
- The method outperforms existing state-of-the-art approaches in both qualitative and quantitative evaluations.
- The multi-disciplinary nature of the concepts involved showcases their relevance in the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Reference: Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style modeling and do not adequately consider style biases across varying speech conditions, which reduce the accuracy of style modeling and hamper the adaptation capability to unseen speakers. To address this, we propose a novel framework, StyleSpeaker, which explicitly extracts speaking styles based on speaker characteristics while accounting for style biases caused by different speeches. Specifically, we utilize a style encoder to capture speakers’ styles from facial motions and enhance them according to motion preferences elicited by varying speech conditions. The enhanced styles are then integrated into the coarse motion features via a style infusion module, which employs a set of style primitives to learn fine-grained style representation. Throughout training, we maintain this set of style primitives to comprehensively model the entire style space. Hence, StyleSpeaker possesses robust style modeling capability for seen speakers and can rapidly adapt to unseen speakers without fine-tuning. Additionally, we design a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. Extensive qualitative and quantitative experiments on three public datasets demonstrate that our method outperforms existing state-of-the-art approaches.