DRSM: efficient neural 4d decomposition for dynamic reconstruction in
  stationary monocular cameras
Weixing Xie Xiao Dong Yong Yang Qiqin Lin Jingze Chen Junfeng Yao Xiaohu Guo
With the popularity of monocular videos generated by video sharing and live
broadcasting applications, reconstructing and editing dynamic scenes in
stationary monocular cameras has become a special but anticipated technology.
In contrast to scene reconstructions that exploit multi-view observations, the
problem of modeling a dynamic scene from a single view is significantly more
under-constrained and ill-posed. Inspired by recent progress in neural
rendering, we present a novel framework to tackle 4D decomposition problem for
dynamic scenes in monocular cameras. Our framework utilizes decomposed static
and dynamic feature planes to represent 4D scenes and emphasizes the learning
of dynamic regions through dense ray casting. Inadequate 3D clues from a
single-view and occlusion are also particular challenges in scene
reconstruction. To overcome these difficulties, we propose deep supervised
optimization and ray casting strategies. With experiments on various videos,
our method generates higher-fidelity results than existing methods for
single-view dynamic scene representation.