We present an algorithm for generating novel views at arbitrary viewpoints
and any input time step given a monocular video of a dynamic scene. Our work
builds upon recent advances in neural implicit representation and uses
continuous and differentiable functions for modeling the time-varying structure
and the appearance of the scene. We jointly train a time-invariant static NeRF
and a time-varying dynamic NeRF, and learn how to blend the results in an
unsupervised manner. However, learning this implicit function from a single
video is highly ill-posed (with infinitely many solutions that match the input
video). To resolve the ambiguity, we introduce regularization losses to
encourage a more physically plausible solution. We show extensive quantitative
and qualitative results of dynamic view synthesis from casually captured
videos.

该研究提出了一种基于神经隐式表示及不可导函数的算法，能够从动态场景中的单目视频中生成任意视角和任意输入时间步长下的新视图；通过联合训练一个不变的静态 NeRF 和一个时间变化的动态 NeRF，以及使用正则化约束的方式解决了样本不足的问题，其在大量数据和质量实验效果上获得了良好的表现。