We present Knowledge NeRF to synthesize novel views for dynamic
scenes.Reconstructing dynamic 3D scenes from few sparse views and rendering
them from arbitrary perspectives is a challenging problem with applications in
various domains. Previous dynamic NeRF methods learn the deformation of
articulated objects from monocular videos. However, qualities of their
reconstructed scenes are limited.To clearly reconstruct dynamic scenes, we
propose a new framework by considering two frames at a time.We pretrain a NeRF
model for an articulated object.When articulated objects moves, Knowledge NeRF
learns to generate novel views at the new state by incorporating past knowledge
in the pretrained NeRF model with minimal observations in the present state. We
propose a projection module to adapt NeRF for dynamic scenes, learning the
correspondence between pretrained knowledge base and current states.
Experimental results demonstrate the effectiveness of our method in
reconstructing dynamic 3D scenes with 5 input images in one state. Knowledge
NeRF is a new pipeline and promising solution for novel view synthesis in
dynamic articulated objects. The data and implementation are publicly available
at this https URL

通过利用 Knowledge NeRF 模型和仅有少量观测状态，在动态场景中成功重建 3D 场景并生成新视角。