Vividportraits: Face Parsing Guided Portrait Animation

Xuze Tian, Jinshan Zhang, Tao Jiang, Boxi Wu, Meng Xi, Zejian Li, Jianwei Yin

Keywords: face parsing; portrait animation; diffusion model

Abstract: Portrait animation aims to transfer the facial expressions and movements of a target character onto a reference character. This task presents two main challenges: accurately transferring motion and expressions while fully preserving the identity features of the reference portrait. We introduce Vividportraits, a diffusion-based model designed to effectively meet these objectives. In contrast to existing methods that rely on sparse representations such as facial landmarks, our approach leverages facial parsing maps for motion guidance, enabling a more precise conveyance of subtle expressions. A random scaling technique is applied during training to prevent the model from internalizing identity-specific features from the driving images. Furthermore, we perform foreground-background segmentation on the reference portrait to reduce data redundancy. The long-video generation process is refined to improve consistency across sequences. Our model, exclusively trained on public datasets, demonstrates superior performance relative to current state-of-the-art methods, achieving a notable 8\% improvement in expression metric. More visual results are available on the anonymous website https://www.vividportraits.cn.

Primary Area: generative models

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Submission Number: 2986