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We present a novel neural radiance model that is trainable in aself-supervised manner for novel-view synthesis of dynamic unstructured scenes.Our end-to-end trainable algorithm learns highly complex, real-world staticscenes within seconds and dynamic scenes with both rigid and non-rigid motionwithin minutes. By differentiating between static and motion-centric pixels, wecreate high-quality representations from a sparse set of images. We performextensive qualitative and quantitative evaluation on existing benchmarks andset the state-of-the-art on performance measures on the challenging NVIDIADynamic Scenes Dataset. Additionally, we evaluate our model performance onchallenging real-world datasets such as Cholec80 and SurgicalActions160.

wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data