  Neural radiance fields (NeRF) achieve highly photo-realistic novel-view
synthesis, but it's a challenging problem to edit the scenes modeled by
NeRF-based methods, especially for dynamic scenes. We propose editable neural
radiance fields that enable end-users to easily edit dynamic scenes and even
support topological changes. Input with an image sequence from a single camera,
our network is trained fully automatically and models topologically varying
dynamics using our picked-out surface key points. Then end-users can edit the
scene by easily dragging the key points to desired new positions. To achieve
this, we propose a scene analysis method to detect and initialize key points by
considering the dynamics in the scene, and a weighted key points strategy to
model topologically varying dynamics by joint key points and weights
optimization. Our method supports intuitive multi-dimensional (up to 3D)
editing and can generate novel scenes that are unseen in the input sequence.
Experiments demonstrate that our method achieves high-quality editing on
various dynamic scenes and outperforms the state-of-the-art. Our code and
captured data are available at https://chengwei-zheng.github.io/EditableNeRF/.
