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Abstract

In this tech report, we present the current state of our ongoing work on
reconstructing Neural Radiance Fields (NERF) of general non-rigid scenes via
ray bending. Non-rigid NeRF (NR-NeRF) takes RGB images of a deforming object
(e.g., from a monocular video) as input and then learns a geometry and
appearance representation that not only allows to reconstruct the input
sequence but also to re-render any time step into novel camera views with high
fidelity. In particular, we show that a consumer-grade camera is sufficient to
synthesize convincing bullet-time videos of short and simple scenes. In
addition, the resulting representation enables correspondence estimation across
views and time, and provides rigidity scores for each point in the scene. We
urge the reader to watch the supplemental videos for qualitative results. We
will release our code.