GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
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Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
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/.
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Publication status
published
External links
Editor
Book title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal / series
Volume
Pages / Article No.
8952 - 8963
Publisher
IEEE
Event
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Neural rendering; Head avatar; Avatar editing; 3D computer vision
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
Notes
Funding
Related publications and datasets
Is supplemented by: https://zju3dv.github.io/geneavatar/