Audio-driven 3D facial animation is crucial for enhancing the metaverse's realism, immersion, and interactivity. While most existing methods focus on generating highly realistic and lively 2D talking head videos by leveraging extensive 2D video datasets these approaches work in pixel space and are not easily adaptable to 3D environments. We present VASA-Rig, which has achieved a significant advancement in the realism of lip-audio synchronization, facial dynamics, and head movements. In particular, we introduce a novel rig parameter-based emotional talking face dataset and propose the Latents2Rig model, which facilitates the transformation of 2D facial animations into 3D. Unlike mesh-based models, VASA-Rig outputs rig parameters, instantiated in this paper as 174 Metahuman rig parameters, making it more suitable for integration into industry-standard pipelines. Extensive experimental results demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of both realism and accuracy.

Audio-driven emotional 3D face animation aims to generate emotionally expressive talking heads with synchronized lip movements. However, previous research has often overlooked the influence of diverse emotions on facial expressions or proved unsuitable for driving MetaHuman models. In response to this deficiency, we introduce EmoFace, a novel audio-driven methodology for creating facial animations with vivid emotional dynamics. Our approach can generate facial expressions with multiple emotions, and has the ability to generate random yet natural blinks and eye movements, while maintaining accurate lip synchronization. We propose independent speech encoders and emotion encoders to learn the relationship between audio, emotion and corresponding facial controller rigs, and finally map into the sequence of controller values. Additionally, we introduce two post-processing techniques dedicated to enhancing the authenticity of the animation, particularly in blinks and eye movements. Furthermore, recognizing the scarcity of emotional audio-visual data suitable for MetaHuman model manipulation, we contribute an emotional audio-visual dataset and derive control parameters for each frames. Our proposed methodology can be applied in producing dialogues animations of non-playable characters (NPCs) in video games, and driving avatars in virtual reality environments. Our further quantitative and qualitative experiments, as well as an user study comparing with existing researches show that our approach demonstrates superior results in driving 3D facial models. The code and sample data are available at https://github.com/SJTU-Lucy/EmoFace