We present PEGASUS, a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g., hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a personalized generative avatar. We generate a synthetic video collection of the target identity with varying facial attributes, where the videos are synthesized by borrowing the attributes from monocular videos of diverse identities. Then, we build a person-specific generative 3D avatar that can modify its attributes continuously while preserving its identity. Through extensive experiments, we demonstrate that our method of generating a synthetic database and creating a 3D generative avatar is the most effective in preserving identity while achieving high realism. Subsequently, we introduce a zero-shot approach to achieve the same goal of generative modeling more efficiently by leveraging a previously constructed personalized generative model.

翻译：我们提出PEGASUS方法，用于从单目视频源构建个性化生成式三维人脸虚拟化身。该生成式三维化身能够实现解耦控制，在保持身份特征的同时选择性改变面部属性（例如发型或鼻型）。我们的方法包含两个阶段：合成数据库生成与个性化生成式化身构建。首先，我们为目标身份生成具有不同面部属性的合成视频集合，这些视频通过借用多样身份的单目视频中的属性合成。随后，我们构建一个可连续修改属性同时保持身份特征的人物专属生成式三维化身。通过大量实验证明，我们提出的合成数据库生成与三维生成式化身创建方法在保持身份特征的同时实现高度真实性方面最为有效。在此基础上，我们引入零样本方法，通过利用先前构建的个性化生成模型，以更高效的方式实现生成式建模的相同目标。