Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for varieties of in-the-wild scenes, including thin structures, view-dependent effects, and complex degrees of motion. We conduct a number of experiments that demonstrate our approach significantly outperforms recent monocular view synthesis methods, and show qualitative results of space-time view synthesis on a variety of real-world videos.

1. Introduction

The topic of novel view synthesis has recently seen impressive progress due to the use of neural networks to learn representations that are well suited for view synthesis tasks. Most prior approaches in this domain make the assumption that the scene is static, or that it is observed from multiple synchronized input views. However, these restrictions are violated by most videos shared on the Internet today, which frequently feature scenes with diverse dynamic content (e.g., humans, animals, vehicles), recorded by a single camera.