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Neural radiance fields (NeRF) achieve highly photo-realistic novel-viewsynthesis, but it's a challenging problem to edit the scenes modeled byNeRF-based methods, especially for dynamic scenes. We propose editable neuralradiance fields that enable end-users to easily edit dynamic scenes and evensupport topological changes. Input with an image sequence from a single camera,our network is trained fully automatically and models topologically varyingdynamics using our picked-out surface key points. Then end-users can edit thescene by easily dragging the key points to desired new positions. To achievethis, we propose a scene analysis method to detect and initialize key points byconsidering the dynamics in the scene, and a weighted key points strategy tomodel topologically varying dynamics by joint key points and weightsoptimization. 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 onvarious dynamic scenes and outperforms the state-of-the-art. Our code andcaptured data are available at https://chengwei-zheng.github.io/EditableNeRF/.

EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points