PEGASUS: Personalized Generative 3D Avatars with Composable Attributes

Seoul National University
CVPR 2024
PEGASUS Teaser Image

We propose a personalized generative 3D face avatars from monocular video sources.

Abstract

We present, PEGASUS, a method for constructing personalized generative 3D face avatars from monocular video sources. As a compositional generative model, our model enables disentangled controls to selectively alter the facial attributes (e.g., hair or nose) of the target individual, while preserving the identity. We present two key approaches to achieve this goal. First, we present a method to construct a person-specific generative 3D avatar by building a synthetic video collection of the target identity with varying facial attributes, where the videos are synthesized by borrowing parts from diverse individuals from other monocular videos. Through several experiments, we demonstrate the superior performance of our approach by generating unseen attributes with high realism. Subsequently, we introduce a zero-shot approach to achieve the same generative modeling more efficiently by leveraging a previously constructed personalized generative model.

Video Presentation

BibTeX

@InProceedings{Cha_2024_CVPR,
  author    = {Cha, Hyunsoo and Kim, Byungjun and Joo, Hanbyul},
  title     = {PEGASUS: Personalized Generative 3D Avatars with Composable Attributes},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2024},
  pages     = {1072-1081}
}