InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes
Mohamad Shahbazi Liesbeth Claessens Michael Niemeyer Edo Collins Alessio Tonioni Luc Van Gool Federico Tombari
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.