3. Dynamic Image-Based Rendering

Given a monocular video of a dynamic scene with
frames (I1, I2, . . . , IN ) and known camera parameters
(P1, P2, . . . , PN ), our goal is to synthesize a novel view-
point at any desired time within the video. Like many other
approaches, we train per-video, ﬁrst optimizing a model to
reconstruct the input frames, then using this model to render
novel views.

Rather than encoding 3D color and density directly in
the weights of an MLP as in recent dynamic NeRF methods,
we integrate classical IBR ideas into a volumetric rendering
framework. Compared to explicit surfaces, volumetric repre-
sentations can more readily model complex scene geometry
with view-dependent effects.
DynIBaR-NeuralDynamicImage-BasedRendering