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We introduce a novel approach for monocular novel view synthesis of dynamicscenes. Existing techniques already show impressive rendering quality but tendto focus on optimization within a single scene without leveraging priorknowledge. This limitation has been primarily attributed to the lack ofdatasets of dynamic scenes available for training and the diversity of scenedynamics. Our method FlowIBR circumvents these issues by integrating a neuralimage-based rendering method, pre-trained on a large corpus of widely availablestatic scenes, with a per-scene optimized scene flow field. Utilizing this flowfield, we bend the camera rays to counteract the scene dynamics, therebypresenting the dynamic scene as if it were static to the rendering network. Theproposed method reduces per-scene optimization time by an order of magnitude,achieving comparable results to existing methods - all on a singleconsumer-grade GPU.

FlowIBR: Leveraging Pre-Training for Efficient Neural Image-Based Rendering of Dynamic Scenes