InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
(2401.05335)Abstract
We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes. Based on a user-provided textual description and a 2D bounding box in a reference viewpoint, InseRF generates new objects in 3D scenes. Recently, methods for 3D scene editing have been profoundly transformed, owing to the use of strong priors of text-to-image diffusion models in 3D generative modeling. Existing methods are mostly effective in editing 3D scenes via style and appearance changes or removing existing objects. Generating new objects, however, remains a challenge for such methods, which we address in this study. Specifically, we propose grounding the 3D object insertion to a 2D object insertion in a reference view of the scene. The 2D edit is then lifted to 3D using a single-view object reconstruction method. The reconstructed object is then inserted into the scene, guided by the priors of monocular depth estimation methods. We evaluate our method on various 3D scenes and provide an in-depth analysis of the proposed components. Our experiments with generative insertion of objects in several 3D scenes indicate the effectiveness of our method compared to the existing methods. InseRF is capable of controllable and 3D-consistent object insertion without requiring explicit 3D information as input. Please visit our project page at https://mohamad-shahbazi.github.io/inserf.
Overview
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InseRF introduces a method for inserting objects into 3D scenes guided by textual descriptions with high consistency and realism.
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The process begins with a 2D edit based on text and a bounding box, which is then transformed into a 3D model without needing explicit 3D coordinates.
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An optional refinement phase can optimize the object's scale, rotation, and translation for better integration into the scene.
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Experimental results demonstrate superior precision and integration of objects in various real-world scenes compared to other methods.
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Future enhancements could include better generative models and techniques for more realistic shadowing and harmonization.
Introduction to InseRF
Within the realm of 3D scene generation and manipulation, a significant innovation has been made with a method designed for the generative insertion of objects into 3D scenes using textual descriptions. The technology, known aptly as InseRF, overcomes existing hurdles of generating new objects within a scene with remarkable consistency and realism.
The Approach
InseRF is distinct in its approach. It begins with a 2D edit of the object based on text and a bounding box in a reference view. This image is then transformed into a 3D model using a single-view reconstruction mechanism. Significantly, this process does not demand explicit 3D coordinates for placement, relying rather on monocular depth estimation methods to guide the object's insertion into the scene.
To further strengthen the inserted object's fidelity, InseRF includes an optional refinement phase, optimizing the newly introduced object's scale, rotation, and translation to better meld it with the existing environment, enhancing the realism of the insertion.
Experimental Findings
The evaluation of InseRF involved testing on various real-world indoor and outdoor scenes. The results underscored InseRF's capacity to insert objects into complex scenes with precision, purely steered by a textual description without explicit spatial 3D information. Visual comparisons underline the superiority of InseRF against other methods, with the ability to seamlessly integrate the new objects within the scene's fabric.
Looking Ahead
Despite substantial strides made by InseRF, its performance aligns closely with the capabilities of underlying generative models. Improvements in these arenas will directly augment InseRF's efficacy. Moreover, incorporating advancements in scene-consistent shadowing and harmonization could further polish the authenticity of the inserted objects.
InseRF ushers in the future of editable 3D environments, where the limits of alteration and creation are expanded exponentially, offering vast new potentials in digital content creation and virtual environment design.
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