NeurIPS 2024poster1 citations
ReplaceAnything3D: Text-Guided Object Replacement in 3D Scenes with Compositional Scene Representations
Edward Bartrum, Thu Nguyen-Phuoc, Chris Xie, Zhengqin Li, Numair Khan, Armen Avetisyan, Douglas Lanman, Lei Xiao
Abstract
We introduce ReplaceAnything3D model RAM3D, a novel method for 3D object replacement in 3D scenes based on users' text description. Given multi-view images of a scene, a text prompt describing the object to replace, and another describing the new object, our Erase-and-Replace approach can effectively swap objects in 3D scenes with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of RAM3D by applying it to various realistic 3D scene types, showcasing results of modified objects that blend in seamlessly with the scene without impacting its overall integrity.
3D inpaintingText-to-3DDiffusionScore-based Distillation3D scenes
BibTeX
@inproceedings{
bartrum2024replaceanythingd,
title={ReplaceAnything3D: Text-Guided Object Replacement in 3D Scenes with Compositional Scene Representations},
author={Edward Bartrum and Thu Nguyen-Phuoc and Chris Xie and Zhengqin Li and Numair Khan and Armen Avetisyan and Douglas Lanman and Lei Xiao},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=8HwI6UavYc}
}