  User-friendly 3D object editing is a challenging task that has attracted
significant attention recently. The limitations of direct 3D object editing
without 2D prior knowledge have prompted increased attention towards utilizing
2D generative models for 3D editing. While existing methods like Instruct
NeRF-to-NeRF offer a solution, they often lack user-friendliness, particularly
due to semantic guided editing. In the realm of 3D representation, 3D Gaussian
Splatting emerges as a promising approach for its efficiency and natural
explicit property, facilitating precise editing tasks. Building upon these
insights, we propose DragGaussian, a 3D object drag-editing framework based on
3D Gaussian Splatting, leveraging diffusion models for interactive image
editing with open-vocabulary input. This framework enables users to perform
drag-based editing on pre-trained 3D Gaussian object models, producing modified
2D images through multi-view consistent editing. Our contributions include the
introduction of a new task, the development of DragGaussian for interactive
point-based 3D editing, and comprehensive validation of its effectiveness
through qualitative and quantitative experiments.
