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CVPR2023Top-tier venue

Local 3D Editing via 3D Distillation of CLIP Knowledge

Junha Hyung, Sungwon Hwang, Daejin Kim, Hyunji Lee, Jaegul Choo

2023Year
5Top-tier citations

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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