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Neural Trajectory Fields for Dynamic Novel View Synthesis

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arxiv 2105.05994 v1 pith:MSIUMXPJ submitted 2021-05-12 cs.CV

Chaoyang Wang , Ben Eckart , Simon Lucey , Orazio Gallo This is my paper · ORCID
classification cs.CV
keywords dynamicneuralscenessequenceabilityallowsapproachesboundaries
verification ladder T0 review T1 audit T2 compute T3 formal

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Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes. The ability to recreate moments, that is, time-varying sequences, is perhaps an even more interesting scenario, but it remains largely unsolved. We introduce DCT-NeRF, a coordinatebased neural representation for dynamic scenes. DCTNeRF learns smooth and stable trajectories over the input sequence for each point in space. This allows us to enforce consistency between any two frames in the sequence, which results in high quality reconstruction, particularly in dynamic regions.

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Cited by 19 Pith papers

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