Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction. Nonetheless, cutting-edge dynamic neural rendering methods rely heavily on these implicit representations, which frequently struggle to capture the intricate details of objects in the scene. Furthermore, implicit methods have difficulty achieving real-time rendering in general dynamic scenes, limiting their use in a variety of tasks. To address the issues, we propose a deformable 3D Gaussians splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with…

• NN
  National Natural Science Foundation of China

  Award: 62036010
• FP
  Fundo para o Desenvolvimento das Ciências e da Tecnologia

  Award: 0002/2023/AKP