AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding

TL;DR

AniTalker is a novel framework that generates realistic, diverse talking faces from a single portrait by employing a universal, identity-decoupled motion representation and a diffusion-based animation generator.

Contribution

It introduces a universal facial motion encoding method that captures complex expressions and head movements, reducing reliance on labeled data and enabling controllable, diverse facial animations.

Findings

Effective in capturing subtle facial expressions and head movements.

Reduces need for labeled data through self-supervised learning.

Produces realistic and diverse talking face animations.

Abstract

The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait. Unlike existing models that primarily focus on verbal cues such as lip synchronization and fail to capture the complex dynamics of facial expressions and nonverbal cues, AniTalker employs a universal motion representation. This innovative representation effectively captures a wide range of facial dynamics, including subtle expressions and head movements. AniTalker enhances motion depiction through two self-supervised learning strategies: the first involves reconstructing target video frames from source frames within the same identity to learn subtle motion representations, and the second develops an identity encoder using metric learning while actively minimizing mutual information between the identity and motion encoders. This approach ensures that the motion representation is dynamic and devoid of identity-specific details, significantly reducing the need for labeled data. Additionally, the integration of a diffusion model with a variance adapter allows for the generation of diverse and controllable facial animations. This method not only demonstrates AniTalker's capability to create detailed and realistic facial movements but also underscores its potential in crafting dynamic avatars for real-world applications. Synthetic results can be viewed at https://github.com/X-LANCE/AniTalker.