Summary of Generative Object Insertion in Gaussian Splatting with a Multi-view Diffusion Model, by Hongliang Zhong et al.
Generative Object Insertion in Gaussian Splatting with a Multi-View Diffusion Model
by Hongliang Zhong, Can Wang, Jingbo Zhang, Jing Liao
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method for inserting new objects into 3D content using Gaussian Splatting. The existing methods, which rely on SDS optimization or single-view inpainting, struggle to produce high-quality results. To address this, the authors introduce a multi-view diffusion model called MVInpainter, built upon a pre-trained stable video diffusion model. This model enables view-consistent object inpainting and is further refined using a ControlNet-based conditional injection module. The paper also proposes a mask-aware 3D reconstruction technique to refine Gaussian Splatting reconstruction from sparse inpainted views. The approach yields diverse results, ensures view-consistent and harmonious insertions, and produces better object quality. Extensive experiments demonstrate that the proposed method outperforms existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create new objects in 3D scenes by using a special way of inserting things into 3D content called Gaussian Splatting. Right now, most methods for doing this are not very good and can’t produce high-quality results. The authors of the paper came up with a new method that uses a special model to make sure the new objects look like they belong in the scene. This model is based on another model that was already really good at making videos stable. They also added some extra steps to make sure the new objects fit in with the rest of the scene and looked realistic. The paper shows that this method works better than other methods. |
Keywords
» Artificial intelligence » Diffusion model » Mask » Optimization
