SadTalker is an audio-driven talking head generator that produces synchronized speaking videos from a single source image and an input audio file. The system utilizes a deep learning framework to map speech signals to facial motion data, enabling the creation of lifelike digital avatars and animated characters.
The project distinguishes itself by employing a three-dimensional morphable model to translate audio features into precise facial landmarks and head pose parameters. It integrates latent diffusion motion synthesis to generate naturalistic head movements and uses expression-aware texture warping to maintain identity consistency while animating complex facial gestures.
The system covers a broad range of animation capabilities, including the synthesis of rhythmic lip movements and stylized head motions that align with the tone of the provided audio. It incorporates neural rendering and temporal consistency filtering to ensure fluid transitions and high-fidelity visual output across generated video frames.
