Neural radiance field is an emerging rendering method that generates
high-quality multi-view consistent images from a neural scene representation
and volume rendering. Although neural radiance field-based techniques are
robust for scene reconstruction, their ability to add or remove objects remains
limited. This paper proposes a new language-driven approach for object
manipulation with neural radiance fields through dataset updates. Specifically,
to insert a new foreground object represented by a set of multi-view images
into a background radiance field, we use a text-to-image diffusion model to
learn and generate combined images that fuse the object of interest into the
given background across views. These combined images are then used for refining
the background radiance field so that we can render view-consistent images
containing both the object and the background. To ensure view consistency, we
propose a dataset updates strategy that prioritizes radiance field training
with camera views close to the already-trained views prior to propagating the
training to remaining views. We show that under the same dataset updates
strategy, we can easily adapt our method for object insertion using data from
text-to-3D models as well as object removal. Experimental results show that our
method generates photorealistic images of the edited scenes, and outperforms
state-of-the-art methods in 3D reconstruction and neural radiance field
blending