3D Geometry-Aware Deformable Gaussian Splatting for Dynamic View Synthesis
Authors: Zhicheng Lu, Xiang Guo, Le Hui, Tianrui Chen, Min Yang, Xiao Tang, Feng Zhu, Yuchao Dai

In this paper we propose a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis. Existing neural radiance fields (NeRF) based solutions learn the deformation in an implicit manner which cannot incorporate 3D scene geometry. Therefore the learned deformation is not necessarily geometrically coherent which results in unsatisfactory dynamic view synthesis and 3D dynamic reconstruction. Recently 3D Gaussian Splatting provides a new representation of the 3D scene building upon which the 3D geometry could be exploited in learning the complex 3D deformation. Specifically the scenes are represented as a collection of 3D Gaussian where each 3D Gaussian is optimized to move and rotate over time to model the deformation. To enforce the 3D scene geometry constraint during deformation we explicitly extract 3D geometry features and integrate them in learning the 3D deformation. In this way our solution achieves 3D geometry-aware deformation modeling which enables improved dynamic view synthesis and 3D dynamic reconstruction. Extensive experimental results on both synthetic and real datasets prove the superiority of our solution which achieves new state-of-the-art performance. The project is available at \href https://npucvr.github.io/GaGS/  https://npucvr.github.io/GaGS/ .