  Bullet time is a type of visual effect commonly used in film, television and
games that makes time seem to slow down or stop while still preserving dynamic
details in the scene. It usually requires multiple sets of cameras to move
slowly with the subject and is synthesized using post-production techniques,
which is costly and one-time. The dynamic scene perspective reconstruction
technology based on neural rendering field can be used to solve this
requirement, but most of the current methods are poor in reconstruction
accuracy due to the blurred input image and overfitting of dynamic and static
regions. Based on the NSFF algorithm, this paper reconstructed the common time
special effects scenes in movies and television from a new perspective. To
improve the accuracy of the reconstructed images, fuzzy kernel was added to the
network for reconstruction and analysis of the fuzzy process, and the clear
perspective after analysis was input into the NSFF to improve the accuracy. By
using the optical flow prediction information to suppress the dynamic network
timely, the network is forced to improve the reconstruction effect of dynamic
and static networks independently, and the ability to understand and
reconstruct dynamic and static scenes is improved. To solve the overfitting
problem of dynamic and static scenes, a new dynamic and static cross entropy
loss is designed. Experimental results show that compared with original NSFF
and other new perspective reconstruction algorithms of dynamic scenes, the
improved NSFF-RFCT improves the reconstruction accuracy and enhances the
understanding ability of dynamic and static scenes.
