SadTalker - An open-source AI digital human project that lets photos speak with a single click.

What is SadTalker?

SadTalker is an open-source AI digital human project jointly launched by Xi'an Jiaotong University, Tencent AI Lab, and Ant Group. SadTalker focuses on generating realistic speaking facial animations using 3D motion coefficients from a single face image and audio recording. By accurately learning facial expressions through ExpNet and synthesizing different styles of head movements using PoseVAE, SadTalker can create high-quality, stylized video animations. SadTalker also includes a wealth of video demonstrations and ablation studies, showcasing its application effects on multiple languages and datasets.

SadTalker's main functions

• 3D motion coefficient generation: Extract 3D motion coefficients of head posture and facial expressions from audio.
• ExpNetA specially designed network for learning accurate facial expressions from audio.
• PoseVAEA conditional variational autoencoder for synthesizing head motion in different styles.
• 3D facial rendering: Maps 3D motion coefficients to 3D keypoint space for rendering stylized facial animations.
• Multilingual supportIt can process audio input in different languages and generate corresponding speaking animations.

SadTalker's technical principles

• 3D motion coefficient learningSadTalker learns 3D motion coefficients, including head posture and facial expressions, by analyzing audio signals. These are key parameters in 3D morphological models (3DMMs).
• ExpNet (Emoji Network)ExpNet is used to extract facial expression information from audio. By learning the mapping relationship between audio and facial expressions, it can generate accurate facial expression animations.
• PoseVAE (Head Pose Variational Autoencoder)PoseVAE is a Conditional Variational Autoencoder (CVAE) used to generate head movements in different styles. It can synthesize natural and stylized head poses based on audio signals.
• 3D facial renderingSadTalker uses a novel 3D facial rendering technique that maps learned 3D motion coefficients to a 3D keypoint space. This process involves the geometry and texture information of the face to generate realistic facial animations.
• Multimodal learningSadTalker considers both audio and visual information during training, using multimodal learning to improve the naturalness and accuracy of animations.
• StylizationSadTalker can generate different styles of facial animations as needed, involving non-linear transformations of facial features and movements to adapt to different visual styles.
• Unsupervised learningSadTalker uses an unsupervised learning method to generate 3D keypoints, which means that it can learn effective motion patterns without a large amount of labeled data.
• Data fusionBy combining audio and visual data, SadTalker can generate animated speaking faces that are synchronized with the audio and have natural expressions.

SadTalker's project address

Application scenarios of SadTalker

• Virtual assistants and customer serviceProvide realistic facial animations for virtual assistants or online customer service to enhance the user experience.
• Video productionIn video production, SadTalker can be used to generate facial animations for characters, saving the cost and time of traditional motion capture.
• Language learning applicationsIt provides pronunciation and facial expressions for different languages to language learning software, helping learners to better understand and imitate them.
• Social media and entertainmentUsers can create personalized virtual avatars for sharing on social media or entertainment content.
• Education and trainingIn remote teaching or online training, SadTalker can provide instructors with virtual avatars to enhance interactivity.