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Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates

CVPR 2024(2024)

The Hong Kong University of Science and Technology | Hong Kong University of Science and Technology | Trinity College Dublin | Deakin University | The Hong Kong University of Science and Technology (HKUST

Cited 3|Views16
Abstract
Neural radiance field (NeRF) is an emerging technique for 3D scene reconstruction and modeling. However, current NeRF-based methods are limited in the capabilities of adding or removing objects. This paper fills the aforementioned gap by proposing a new language-driven method for object manipulation in NeRFs through dataset updates. Specifically, to insert an object represented by a set of multi-view images into a background NeRF, we use a text-to-image diffusion model to blend the object into the given background across views. The generated images are then used to update the NeRF so that we can render view-consistent images of the object within the background. To ensure view consistency, we propose a dataset update strategy that prioritizes the radiance field training based on camera poses in a pose-ordered manner. We validate our method in two case studies: object insertion and object removal. Experimental results show that our method can generate photo-realistic results and achieves state-of-the-art performance in NeRF editing.
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Key words
Radiance Field,Neural Radiance Fields,Image Object,Diffusion Model,3D Scene,Camera Pose,Multi-view Images,Convolutional Neural Network,Generative Adversarial Networks,Target Object,Background Image,Image Synthesis,Sneakers,Insertion Method,Image Inpainting,3D Bounding Box,Traditional Pipeline,Traditional 3D,Masked Area
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Chat Paper

要点】:本文提出了一种基于语言的物体融合方法,通过数据集更新实现神经辐射场中的物体操作,提高了场景重建的灵活性。

方法】:利用文本到图像的扩散模型学习并生成将新物体融合到背景中的多视角图像,进而更新背景辐射场,以实现视角一致的渲染效果。

实验】:通过实验使用不同数据集(未明确指出数据集名称),证明了该方法在物体插入和移除方面均能生成逼真图像,并在3D重建和神经辐射场融合方面优于现有技术。