GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
<|reference_start|>GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image: Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars. However, due to the diversity of frameworks, there is no practical method to support high-level applications like 3D head avatar editing across different representations. In this paper, we propose a generic avatar editing approach that can be universally applied to various 3DMM driving volumetric head avatars. To achieve this goal, we design a novel expression-aware modification generative model, which enables lift 2D editing from a single image to a consistent 3D modification field. To ensure the effectiveness of the generative modification process, we develop several techniques, including an expression-dependent modification distillation scheme to draw knowledge from the large-scale head avatar model and 2D facial texture editing tools, implicit latent space guidance to enhance model convergence, and a segmentation-based loss reweight strategy for fine-grained texture inversion. Extensive experiments demonstrate that our method delivers high-quality and consistent results across multiple expression and viewpoints. Project page: https://zju3dv.github.io/geneavatar/<|reference_end|>
@article{bao2024geneavatar:,
    title={GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from
  a Single Image},
    author={Chong Bao, Yinda Zhang, Yuan Li, Xiyu Zhang, Bangbang Yang, Hujun Bao,
  Marc Pollefeys, Guofeng Zhang, Zhaopeng Cui},
    journal={arXiv preprint arXiv:2404.02152},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02152},
    primaryClass={cs.CV}
}