Neural Processes combine the strengths of neural networks and Gaussian
processes to achieve both flexible learning and fast prediction in stochastic
processes. However, a large class of problems comprises underlying temporal
dependency structures in a sequence of stochastic processes that Neural
Processes (NP) do not explicitly consider. In this paper, we propose Sequential
Neural Processes (SNP) which incorporates a temporal state-transition model of
stochastic processes and thus extends its modeling capabilities to dynamic
stochastic processes. In applying SNP to dynamic 3D scene modeling, we
introduce the Temporal Generative Query Networks. To our knowledge, this is the
first 4D model that can deal with the temporal dynamics of 3D scenes. In
experiments, we evaluate the proposed methods in dynamic (non-stationary)
regression and 4D scene inference and rendering.

本文提出了一种 Sequential Neural Processes (SNP) 的方法，它通过引入一个时间状态转移模型来扩展 Neural Processes (NP) 的建模能力，以处理动态随机过程。作者还使用 Temporal Generative Query Networks 将 SNP 应用于动态三维场景建模，实现了可以处理 3D 场景时间动态的 4D 模型，并在实验中验证了该方法在动态回归和 4D 场景推断与渲染中的性能。