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.

该论文提出了一种新的非刚性动态场景重建和新视角合成方法 ——NR-NeRF，该方法能够使用一个便携式消费级相机拍摄到的动态场景的 RGB 图像创建高质量的时空几何和外观表示，并且可以生成复杂的渲染场景。