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arXiv · 2404.19204

NeRF-Insert: 3D Local Editing with Multimodal Control Signals

Benet Oriol Sabat·Alessandro Achille·Matthew Trager·Stefano Soatto
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Abstract

We propose NeRF-Insert, a NeRF editing framework that allows users to make high-quality local edits with a flexible level of control. Unlike previous work that relied on image-to-image models, we cast scene editing as an in-painting problem, which encourages the global structure of the scene to be preserved. Moreover, while most existing methods use only textual prompts to condition edits, our framework accepts a combination of inputs of different modalities as reference. More precisely, a user may provide a combination of textual and visual inputs including images, CAD models, and binary image masks for specifying a 3D region. We use generic image generation models to in-paint the scene from multiple viewpoints, and lift the local edits to a 3D-consistent NeRF edit. Compared to previous methods, our results show better visual quality and also maintain stronger consistency with the original NeRF.

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Benet Oriol Sabat, Alessandro Achille, Matthew Trager, Stefano Soatto. 2024-04-30. NeRF-Insert: 3D Local Editing with Multimodal Control Signals. https://arxiv.org/abs/2404.19204

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