DragGaussian: Enabling Drag-style Manipulation on 3D Gaussian Representation
This work addresses the problem of interactive 3D editing for users in computer graphics, offering a more user-friendly approach compared to existing methods, though it appears incremental by building on 3D Gaussian Splatting and diffusion models.
The authors tackled the challenge of user-friendly 3D object editing by proposing DragGaussian, a framework that enables drag-style manipulation on 3D Gaussian representations, resulting in modified 2D images with multi-view consistency.
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.