As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …

Abstract Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel
view synthesis. While NeRFs are quickly being adapted for a wider set of applications …

We propose NeRFiller an approach that completes missing portions of a 3D capture via
generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a …

We show that physics-based simulations can be seamlessly integrated with NeRF to
generate high-quality elastodynamics of real-world objects. Unlike existing methods we …

Neural Radiance Field (NeRF) has achieved substantial progress in novel view synthesis
given multi-view images. Recently, some works have attempted to train a NeRF from a …

This review thoroughly examines the role of semantically-aware Neural Radiance Fields
(NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It …

Denoising diffusion models have enabled high-quality image generation and editing. We
present a method to localize the desired edit region implicit in a text instruction. We leverage …

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 …

The authors propose Point'n Move, a method that achieves interactive scene object
manipulation with exposed region inpainting. Interactivity here further comes from intuitive …

We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research
on editing 3D objects primarily concentrated on editing individual objects by leveraging off …