TGAvatar: Reconstructing 3D Gaussian Avatars With Transformer-Based Tri-Plane

We introduce TGAvatar, a novel framework for 3D head animation and reconstruction that revolutionizes the use of 3D Gaussian Splatting (3DGS). TGAvatar significantly advances rendering quality by leveraging the intricate properties of 3DGS to achieve detailed and realistic representations of human head geometries and textures. We use an innovative application of linear blending techniques to imitate 3D Morphable Model (3DMM) coefficients within 3DGS, thereby enabling precise and dynamic facial feature and expression modeling. Further enhancing TGAvatar’s capabilities, a transformer based tri-plane module is incorporated to accurately infer spherical harmonics and alpha parameters. This integration is pivotal for the method, as it allows allows us to efficiently and precisely represent the visual characteristics of gaussians, tailored specifically to the intricate details of the head’s components. Our exhaustive evaluations show that TGAvatar not only elevates the fidelity and realism of 3D head reconstructions but also sets a new standard by surpassing existing methods in rendering quality and computational efficiency. Please see our project page at https://hrg0417.github.io/TGAvatar/