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SIn-NeRF2NeRF: Editing 3D Scenes with Instructions through Segmentation and Inpainting
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TL;DR Perform 3D object editing selectively by disentangling it from the background scene. Instruct-NeRF2NeRF (in2n) is a promising method that enables editing of 3D scenes composed of Neural Radiance Field (NeRF) using text prompts. However, it is challenging to perform geometrical modifications such as shrinking, scaling, or moving on both the background and object simultaneously. In this project, we enable geometrical changes of objects within the 3D scene by selectively editing the object after separating it from the scene. We perform object segmentation and background inpainting respectively, and demonstrate various examples of freely resizing or moving disentangled objects within the three-dimensional space.
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Cited by 1 Pith paper
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From 2D to 3D Cognition: A Brief Survey of General World Models
A survey proposing a two-pillar, three-capability framework that organizes recent AI world models by their transition from 2D visual prediction to 3D cognition.
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