**Introduction:** The paper introduces VASA-1, a framework for generating lifelike talking faces using a single static image and a speech audio clip. The primary goal is to produce high-quality, realistic talking face videos with synchronized lip movements, natural facial expressions, and head motions. The method aims to enhance the realism and liveliness of AI-generated avatars, making them more engaging in various applications such as communication, education, and healthcare. **Core Innovations:** - **Diffusion-based Holistic Facial Dynamics and Head Movement Generation:** The framework uses a diffusion model to generate holistic facial dynamics and head movements in a face latent space, capturing a wide range of facial nuances and natural head motions. - **Expressive and Disentangled Face Latent Space:** The method constructs a disentangled and expressive face latent space using a large volume of face videos, enabling the generation of diverse and lifelike talking behaviors. **Method:** - **Task Definition:** The input consists of a face image and a speech audio clip, with optional control signals like gaze direction, head distance, and emotion offset. - **Overall Framework:** The process involves generating holistic facial dynamics and head motion in the latent space, followed by video frame generation using a face decoder. - **Expressive and Disentangled Face Latent Space Construction:** The framework uses a 3D-aided representation to learn a disentangled latent space, ensuring both expressiveness and disentanglement between facial dynamics and other factors. - **Holistic Facial Dynamics Generation with Diffusion Transformer:** The diffusion transformer model is trained on massive talking face videos to generate comprehensive facial dynamics and head poses from audio and control signals. **Evaluation:** - **Qualitative and Quantitative Evaluation:** The method is evaluated using metrics such as audio-lip synchronization, audio-pose alignment, pose variation intensity, and video quality. VASA-1 outperforms existing methods in all evaluated metrics, demonstrating superior performance in generating realistic and lifelike talking faces. **Conclusion:** VASA-1 significantly advances the realism and efficiency of audio-driven talking face generation, paving the way for more natural and engaging interactions between digital avatars and humans. The method's ability to handle out-of-distribution inputs and its controllable conditioning signals further enhance its potential in various applications.**Introduction:** The paper introduces VASA-1, a framework for generating lifelike talking faces using a single static image and a speech audio clip. The primary goal is to produce high-quality, realistic talking face videos with synchronized lip movements, natural facial expressions, and head motions. The method aims to enhance the realism and liveliness of AI-generated avatars, making them more engaging in various applications such as communication, education, and healthcare. **Core Innovations:** - **Diffusion-based Holistic Facial Dynamics and Head Movement Generation:** The framework uses a diffusion model to generate holistic facial dynamics and head movements in a face latent space, capturing a wide range of facial nuances and natural head motions. - **Expressive and Disentangled Face Latent Space:** The method constructs a disentangled and expressive face latent space using a large volume of face videos, enabling the generation of diverse and lifelike talking behaviors. **Method:** - **Task Definition:** The input consists of a face image and a speech audio clip, with optional control signals like gaze direction, head distance, and emotion offset. - **Overall Framework:** The process involves generating holistic facial dynamics and head motion in the latent space, followed by video frame generation using a face decoder. - **Expressive and Disentangled Face Latent Space Construction:** The framework uses a 3D-aided representation to learn a disentangled latent space, ensuring both expressiveness and disentanglement between facial dynamics and other factors. - **Holistic Facial Dynamics Generation with Diffusion Transformer:** The diffusion transformer model is trained on massive talking face videos to generate comprehensive facial dynamics and head poses from audio and control signals. **Evaluation:** - **Qualitative and Quantitative Evaluation:** The method is evaluated using metrics such as audio-lip synchronization, audio-pose alignment, pose variation intensity, and video quality. VASA-1 outperforms existing methods in all evaluated metrics, demonstrating superior performance in generating realistic and lifelike talking faces. **Conclusion:** VASA-1 significantly advances the realism and efficiency of audio-driven talking face generation, paving the way for more natural and engaging interactions between digital avatars and humans. The method's ability to handle out-of-distribution inputs and its controllable conditioning signals further enhance its potential in various applications.