Multi-view reconstruction of bullet time effect based on improved NSFF model

Linquan Yu; Yan Gao; Yangtian Yan; Wentao Zeng

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arXiv · 2304.00330

Multi-view reconstruction of bullet time effect based on improved NSFF model

Linquan Yu·Yan Gao·Yangtian Yan·Wentao Zeng

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Abstract

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.

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BibTeX RIS

Linquan Yu, Yan Gao, Yangtian Yan, Wentao Zeng. 2023-04-01. Multi-view reconstruction of bullet time effect based on improved NSFF model. https://arxiv.org/abs/2304.00330

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