  Dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of
rendering photo-realistic novel view images from a monocular RGB video of a
dynamic scene. Although it warps moving points across frames from the
observation spaces to a common canonical space for rendering, dynamic NeRF does
not model the change of the reflected color during the warping. As a result,
this approach often fails drastically on challenging specular objects in
motion. We address this limitation by reformulating the neural radiance field
function to be conditioned on surface position and orientation in the
observation space. This allows the specular surface at different poses to keep
the different reflected colors when mapped to the common canonical space.
Additionally, we add the mask of moving objects to guide the deformation field.
As the specular surface changes color during motion, the mask mitigates the
problem of failure to find temporal correspondences with only RGB supervision.
We evaluate our model based on the novel view synthesis quality with a
self-collected dataset of different moving specular objects in realistic
environments. The experimental results demonstrate that our method
significantly improves the reconstruction quality of moving specular objects
from monocular RGB videos compared to the existing NeRF models. Our code and
data are available at the project website https://github.com/JokerYan/NeRF-DS.
