EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points

• URL: http://arxiv.org/abs/2212.04247v2
• Date: Tue, 28 Mar 2023 05:14:33 GMT
• Title: EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points
• Authors: Chengwei Zheng, Wenbin Lin, Feng Xu
• Abstract summary: We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes. Our network is trained fully automatically and models topologically varying dynamics using our picked-out surface key points. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence.
• Score: 7.4100592531979625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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