  While high fidelity and efficiency are central to the creation of digital
head avatars, recent methods relying on 2D or 3D generative models often
experience limitations such as shape distortion, expression inaccuracy, and
identity flickering. Additionally, existing one-shot inversion techniques fail
to fully leverage multiple input images for detailed feature extraction. We
propose a novel framework, \textbf{Incremental 3D GAN Inversion}, that enhances
avatar reconstruction performance using an algorithm designed to increase the
fidelity from multiple frames, resulting in improved reconstruction quality
proportional to frame count. Our method introduces a unique animatable 3D GAN
prior with two crucial modifications for enhanced expression controllability
alongside an innovative neural texture encoder that categorizes texture feature
spaces based on UV parameterization. Differentiating from traditional
techniques, our architecture emphasizes pixel-aligned image-to-image
translation, mitigating the need to learn correspondences between observation
and canonical spaces. Furthermore, we incorporate ConvGRU-based recurrent
networks for temporal data aggregation from multiple frames, boosting geometry
and texture detail reconstruction. The proposed paradigm demonstrates
state-of-the-art performance on one-shot and few-shot avatar animation tasks.
Code will be available at https://github.com/XChenZ/invertAvatar.
