AniFaceDiff: Animating Stylized Avatars via Parametric Conditioned Diffusion Models

TL;DR

AniFaceDiff is a novel diffusion-based method that animates stylized avatars by effectively preserving pose and expression details while preventing unintended features, demonstrating superior quality and generalization across diverse styles.

Contribution

The paper introduces AniFaceDiff, a new conditioning framework with facial alignment and expression adaptation to improve stylized avatar animation quality.

Findings

Achieves state-of-the-art animation quality and expression preservation.

Effectively prevents unintended features from target motion.

Demonstrates strong generalization across diverse styles.

Abstract

Animating stylized avatars with dynamic poses and expressions has attracted increasing attention for its broad range of applications. Previous research has made significant progress by training controllable generative models to synthesize animations based on reference characteristics, pose, and expression conditions. However, the mechanisms used in these methods to control pose and expression often inadvertently introduce unintended features from the target motion, while also causing a loss of expression-related details, particularly when applied to stylized animation. This paper proposes a new method based on Stable Diffusion, called AniFaceDiff, incorporating a new conditioning module for animating stylized avatars. First, we propose a refined spatial conditioning approach by Facial Alignment to prevent the inclusion of identity characteristics from the target motion. Then, we introduce an Expression Adapter that incorporates additional cross-attention layers to address the potential loss of expression-related information. Our approach effectively preserves pose and expression from the target video while maintaining input image consistency. Extensive experiments demonstrate that our method achieves state-of-the-art results, showcasing superior image quality, preservation of reference features, and expression accuracy, particularly for out-of-domain animation across diverse styles, highlighting its versatility and strong generalization capabilities. This work aims to enhance the quality of virtual stylized animation for positive applications. To promote responsible use in virtual environments, we contribute to the advancement of detection for generative content by evaluating state-of-the-art detectors, highlighting potential areas for improvement, and suggesting solutions.