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- W4310999989 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/." @default.
- W4310999989 created "2022-12-22" @default.
- W4310999989 creator A5057193669 @default.
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- W4310999989 date "2022-12-07" @default.
- W4310999989 modified "2023-10-18" @default.
- W4310999989 title "EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points" @default.
- W4310999989 doi "https://doi.org/10.48550/arxiv.2212.04247" @default.
- W4310999989 hasPublicationYear "2022" @default.
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