Generative object insertion in Gaussian splatting with a multi-view diffusion model

Hongliang Zhong, Can Wang, Jingbo Zhang, Jing Liao*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

53 Downloads (CityUHK Scholars)

Abstract

Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality results. To address this, we propose a novel method for object insertion in 3D content represented by Gaussian Splatting. Our approach introduces a multi-view diffusion model, dubbed MVInpainter, which is built upon a pre-trained stable video diffusion model to facilitate view-consistent object inpainting. Within MVInpainter, we incorporate a ControlNet-based conditional injection module to enable controlled and more predictable multi-view generation. After generating the multi-view inpainted results, we further propose a mask-aware 3D reconstruction technique to refine Gaussian Splatting reconstruction from these sparse inpainted views. By leveraging these fabricate techniques, our approach yields diverse results, ensures view-consistent and harmonious insertions, and produces better object quality. Extensive experiments demonstrate that our approach outperforms existing methods. © 2025 The Author(s).
Original languageEnglish
Article number100238
JournalVisual Informatics
Volume9
Issue number2
Online published8 Apr 2025
DOIs
Publication statusPublished - Jun 2025

Funding

The work described in this paper was fully supported by a GRF [Project No. CityU 11208123] grant from the Research Grants Council (RGC) of the Hong Kong Special Administrative Region, China [Project No. CityU 11208123].

Research Keywords

  • 3D generation
  • Diffusion model
  • Gaussian splatting
  • Neural rendering

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

RGC Funding Information

  • RGC-funded

Fingerprint

Dive into the research topics of 'Generative object insertion in Gaussian splatting with a multi-view diffusion model'. Together they form a unique fingerprint.

Cite this