Neural Volume Rendering for Dynamic Scenes

NeRF has shown incredible view synthesis results, but it requires multi-view captures for STATIC scenes.

How can we achieve view synthesis for DYNAMIC scenes from a single video? Here is what I learned from several recent efforts.

Instead of presenting Video-NeRF, Nerfie, NR-NeRF, D-NeRF, NeRFlow, NSFF (and many others!) as individual algorithms, here I try to view them from a unifying perspective and understand the pros/cons of various design choices.

Okay, here we go.

*Background*

NeRF represents the scene as a 5D continuous volumetric scene function that maps the spatial position and viewing direction to color and density. It then projects the colors/densities to form an image with volume rendering.

Volumetric + Implicit -> Awesome!

*Model*

Building on NeRF, one can extend it for handling dynamic scenes with two types of approaches.

A) 4D (or 6D with views) function.

One direct approach is to include TIME as an additional input to learn a DYNAMIC radiance field.

e.g., Video-NeRF, NSFF, NeRFlow

B) 3D Template with Deformation.

Inspired by non-rigid reconstruction methods, this type of approach learns a radiance field in a canonical frame (template) and predicts deformation for each frame to account for dynamics over time.

e.g., Nerfie, NR-NeRF, D-NeRF