The field of 3D face and avatar reconstruction is moving towards more accurate and efficient methods for creating and manipulating digital humans. Recent developments have focused on improving the realism and expressiveness of avatars, as well as enhancing their privacy and security. One notable direction is the use of Gaussian Splatting representations, which have been shown to be effective for efficient reconstruction and rendering of 3D faces and avatars. Another area of research is the development of methods for protecting the privacy of 3D facial avatars, such as through the use of adversarial perturbations. The field is also seeing advancements in the reconstruction of topologically consistent facial geometry, which is crucial for digital avatar creation pipelines. Noteworthy papers include AEGIS, which presents a privacy-preserving identity masking framework for 3D Gaussian Avatars, and VGGTFace, which proposes an automatic approach for topologically consistent facial geometry reconstruction from in-the-wild multi-view images. RigAnyFace is also notable for its scalable neural auto-rigging framework for facial meshes of diverse topologies. Additionally, STAvatar and AvatarBrush have made significant contributions to the field of 3D head avatar reconstruction and editing. GFT-GCN and GS-Checker have also made important contributions to the areas of 3D face recognition and tampering localization, respectively.