VASA-1 is a framework for generating lifelike talking faces with appealing visual affective skills using a single static image and a speech audio clip. The core innovation of VASA-1 is a diffusion-based holistic facial dynamics and head movement generation model that operates in a face latent space. This model is trained using videos to create an expressive and disentangled face latent space. VASA-1 produces high-quality, realistic talking face videos with synchronized lip movements and natural head motions. It supports real-time generation of 512x512 videos at up to 40 FPS with minimal latency. The method outperforms existing techniques in various dimensions, including video quality, realism, and controllability. It enables realistic and lifelike interactions with AI avatars that emulate human conversational behaviors. The method uses a 3D-aided representation and carefully designed loss functions to achieve disentanglement and expressiveness in the face latent space. It also incorporates optional conditioning signals such as gaze direction, head distance, and emotion offset to enhance generation controllability. The method is evaluated on multiple datasets and shows superior performance in audio-lip synchronization, head pose alignment, and video quality. It also demonstrates robustness to out-of-distribution inputs. The research has potential applications in communication, education, and healthcare. The method is designed to be responsible and ethical, focusing on positive applications and avoiding misuse. The work contributes to the development of AI that can enhance human well-being.VASA-1 is a framework for generating lifelike talking faces with appealing visual affective skills using a single static image and a speech audio clip. The core innovation of VASA-1 is a diffusion-based holistic facial dynamics and head movement generation model that operates in a face latent space. This model is trained using videos to create an expressive and disentangled face latent space. VASA-1 produces high-quality, realistic talking face videos with synchronized lip movements and natural head motions. It supports real-time generation of 512x512 videos at up to 40 FPS with minimal latency. The method outperforms existing techniques in various dimensions, including video quality, realism, and controllability. It enables realistic and lifelike interactions with AI avatars that emulate human conversational behaviors. The method uses a 3D-aided representation and carefully designed loss functions to achieve disentanglement and expressiveness in the face latent space. It also incorporates optional conditioning signals such as gaze direction, head distance, and emotion offset to enhance generation controllability. The method is evaluated on multiple datasets and shows superior performance in audio-lip synchronization, head pose alignment, and video quality. It also demonstrates robustness to out-of-distribution inputs. The research has potential applications in communication, education, and healthcare. The method is designed to be responsible and ethical, focusing on positive applications and avoiding misuse. The work contributes to the development of AI that can enhance human well-being.