Local 3D Editing via 3D Distillation of CLIP Knowledge
Junha Hyung, Sungwon Hwang, Daejin Kim, Hyunji Lee, Jaegul Choo
2023年份
5顶会引用
摘要
Figure 1 . Our local editing NeRF (LENeRF) enables users to edit specific areas of 3D assets based on textual prompts by estimating a 3D mask for tri-plane features. For instance, given an original 3D radiance field (a), users can define their desired area to edit (underlined text prompt, e.g., "eyes"). LENeRF then generates a 3D mask, which is employed for feature fusion, allowing for targeted modifications that adhere to the editing prompt (e.g., "blue eyes") (b). Additionally, as illustrated in (c), the 3D mask itself can be rendered and visualized for further analysis.
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引用它的顶会 Paper5
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- Transfer CLIP for Generalizable Image DenoisingJun Cheng, Dong Liang, Shan TanCVPR 2024
- GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and GaussiansShuyi Jiang, Qihao Zhao, Hossein Rahmani, De Wen Soh 等ICLR 2025
- SAT3D: Image-driven Semantic Attribute Transfer in 3DZhijun Zhai, Zengmao Wang, Xiaoxiao Long, Kaixuan Zhou 等ACM MM 2024
它引用的顶会 Paper34
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- Efficient Geometry-aware 3D Generative Adversarial NetworksEric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano 等CVPR 2022 · 被引用 984 次
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