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We present a method to perform novel view and time synthesis of dynamicscenes, requiring only a monocular video with known camera poses as input. Todo this, we introduce Neural Scene Flow Fields, a new representation thatmodels the dynamic scene as a time-variant continuous function of appearance,geometry, and 3D scene motion. Our representation is optimized through a neuralnetwork to fit the observed input views. We show that our representation can beused for complex dynamic scenes, including thin structures, view-dependenteffects, and natural degrees of motion. We conduct a number of experiments thatdemonstrate our approach significantly outperforms recent monocular viewsynthesis methods, and show qualitative results of space-time view synthesis ona variety of real-world videos.

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