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

• URL: http://arxiv.org/abs/2011.13084v3
• Date: Wed, 21 Apr 2021 02:11:44 GMT
• Title: Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
• Authors: Zhengqi Li, Simon Niklaus, Noah Snavely, Oliver Wang
• Abstract summary: We introduce 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 complex dynamic scenes, including thin structures, view-dependent effects, and natural degrees of motion.
• Score: 70.76742458931935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 complex dynamic scenes, including thin structures, view-dependent effects, and natural 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.

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