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 language | English |
|---|---|
| Article number | 100238 |
| Journal | Visual Informatics |
| Volume | 9 |
| Issue number | 2 |
| Online published | 8 Apr 2025 |
| DOIs | |
| Publication status | Published - 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
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GRF: Text-to-3D Generation and Manipulation with Neural Radiance Field Representation
LIAO, J. (Principal Investigator / Project Coordinator)
1/01/24 → …
Project: Research
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