Generative Object Insertion in Gaussian Splatting with a Multi-View Diffusion Model
<|reference_start|>Generative Object Insertion in Gaussian Splatting with a Multi-View Diffusion Model: 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.<|reference_end|>
@article{zhong2024generative,
    title={Generative Object Insertion in Gaussian Splatting with a Multi-View
  Diffusion Model},
    author={Hongliang Zhong, Can Wang, Jingbo Zhang, Jing Liao},
    journal={arXiv preprint arXiv:2409.16938},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16938},
    primaryClass={cs.CV cs.AI cs.GR}
}