Local 3D Editing via 3D Distillation of CLIP Knowledge
Junha Hyung, Sungwon Hwang, Daejin Kim, Hyunji Lee, Jaegul Choo
Abstract
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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Install the CLIlune papers fulltext a3d6a2ca-6dfc-4c12-9bf9-506a73ef2ec0Cited by top-tier papers5
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- GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and GaussiansShuyi Jiang, Qihao Zhao, Hossein Rahmani, De Wen Soh et al.ICLR 2025
- SAT3D: Image-driven Semantic Attribute Transfer in 3DZhijun Zhai, Zengmao Wang, Xiaoxiao Long, Kaixuan Zhou et al.ACM MM 2024
Builds on34
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Neural Sparse Voxel FieldsLingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua et al.NeurIPS 2020 · 1,535 citations
- StyleCLIP: Text-Driven Manipulation of StyleGAN ImageryOr Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or et al.ICCV 2021 · 1,437 citations
- GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisKatja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas GeigerNeurIPS 2020 · 1,001 citations
- Efficient Geometry-aware 3D Generative Adversarial NetworksEric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano et al.CVPR 2022 · 984 citations
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