Fine-Grained 3D Gaussian Head Avatars Modeling from Static Captures Via Joint Reconstruction and Registration

duces plausible modeling results, while the prior-free one is used to more freely capture the fine-grained geometric and texture details in the input images. Additionally, we simultaneously solve the registration problem between the two 3D Gaussian sets. On the one hand, the registration results will provide binding information for the prior-free reconstruction to make it animatable. On the other hand, during optimization, the prior-based Gaussian can regularize the prior-free reconstruction to resist overfitting and perform well in novel expressions. Finally, we merge the parts of the prior-based reconstruction that are occluded in the input images with the prior-free reconstruction set and then apply appropriate post-processing strategies (such as teeth enhancement) to produce a complete head avatar. We evaluated our method on the public Nersemble dataset and our own in-the-wild data. The experiments demonstrate that, under the same experimental settings, our method significantly improves modeling quality and provides better support for detailed modeling at higher resolutions.