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In the field of media production, video editing techniques play a pivotalrole. Recent approaches have had great success at performing novel view imagesynthesis of static scenes. But adding temporal information adds an extra layerof complexity. Previous models have focused on implicitly representing staticand dynamic scenes using NeRF. These models achieve impressive results but arecostly at training and inference time. They overfit an MLP to describe thescene implicitly as a function of position. This paper proposes ZeST-NeRF, anew approach that can produce temporal NeRFs for new scenes without retraining.We can accurately reconstruct novel views using multi-view synthesis techniquesand scene flow-field estimation, trained only with unrelated scenes. Wedemonstrate how existing state-of-the-art approaches from a range of fieldscannot adequately solve this new task and demonstrate the efficacy of oursolution. The resulting network improves quantitatively by 15% and producessignificantly better visual results.

ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs