Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

Abstract

We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appearance representation. We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e.g. a `bullet-time' video effect. NR-NeRF disentangles the dynamic scene into a canonical volume and its deformation. Scene deformation is implemented as ray bending, where straight rays are deformed non-rigidly. We also propose a novel rigidity network to better constrain rigid regions of the scene, leading to more stable results. The ray bending and rigidity network are trained without explicit supervision. Our formulation enables dense correspondence estimation across views and time, and compelling video editing applications such as motion exaggeration. Our code will be open sourced.

Project page (incl. supplemental videos and code): https://vcai.mpi-inf.mpg.de/projects/nonrigid_nerf/ or https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/

Cited by in corpus (6)

• HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
• DOVE: Learning Deformable 3D Objects by Watching Videos
• TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
• Neural Fields in Visual Computing and Beyond
• NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
• Learning to Stylize Novel Views