  Neural rendering has demonstrated remarkable success in dynamic scene
reconstruction. Thanks to the expressiveness of neural representations, prior
works can accurately capture the motion and achieve high-fidelity
reconstruction of the target object. Despite this, real-world video scenarios
often feature large unobserved regions where neural representations struggle to
achieve realistic completion. To tackle this challenge, we introduce MorpheuS,
a framework for dynamic 360{\deg} surface reconstruction from a casually
captured RGB-D video. Our approach models the target scene as a canonical field
that encodes its geometry and appearance, in conjunction with a deformation
field that warps points from the current frame to the canonical space. We
leverage a view-dependent diffusion prior and distill knowledge from it to
achieve realistic completion of unobserved regions. Experimental results on
various real-world and synthetic datasets show that our method can achieve
high-fidelity 360{\deg} surface reconstruction of a deformable object from a
monocular RGB-D video.
