  Implicit neural representation has paved the way for new approaches to
dynamic scene reconstruction and rendering. 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 no extra overhead, which can
mitigate the impact of inaccurate poses on the smoothness of time interpolation
tasks in real-world datasets. Through a differential Gaussian rasterizer, the
deformable 3D Gaussians not only achieve higher rendering quality but also
real-time rendering speed. Experiments show that our method outperforms
existing methods significantly in terms of both rendering quality and speed,
making it well-suited for tasks such as novel-view synthesis, time
interpolation, and real-time rendering.
