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MvDrag3D: Drag-based Creative 3D Editing via Multi-view Generation-Reconstruction Priors
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Drag-based editing has become popular in 2D content creation, driven by the capabilities of image generative models. However, extending this technique to 3D remains a challenge. Existing 3D drag-based editing methods, whether employing explicit spatial transformations or relying on implicit latent optimization within limited-capacity 3D generative models, fall short in handling significant topology changes or generating new textures across diverse object categories. To overcome these limitations, we introduce MVDrag3D, a novel framework for more flexible and creative drag-based 3D editing that leverages multi-view generation and reconstruction priors. At the core of our approach is the usage of a multi-view diffusion model as a strong generative prior to perform consistent drag editing over multiple rendered views, which is followed by a reconstruction model that reconstructs 3D Gaussians of the edited object. While the initial 3D Gaussians may suffer from misalignment between different views, we address this via view-specific deformation networks that adjust the position of Gaussians to be well aligned. In addition, we propose a multi-view score function that distills generative priors from multiple views to further enhance the view consistency and visual quality. Extensive experiments demonstrate that MVDrag3D provides a precise, generative, and flexible solution for 3D drag-based editing, supporting more versatile editing effects across various object categories and 3D representations.
Forward citations
Cited by 6 Pith papers
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Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling
RoMaP enables precise and drastic part-level edits in 3D Gaussian scenes using SH-based soft-label 3D segmentation and a regularized SDS loss anchored on scheduled latent-mixing images.
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DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions
DragScene propagates a single-view drag edit across multiple views by reconstructing a coarse point cloud with the edit's latent features, then reconstructing the edited 3D scene.
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GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation
A point-cloud-structured latent space with cascaded flow matching enables high-quality text- and image-conditioned 3D object generation and interactive editing.
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CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation
Coupling a global latent code with a 3D feature volume lets off-the-shelf 3D generators perform local semantic edits — copy, delete, resize, mix, and drag — across object categories while preserving unedited regions.
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CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation
CAGE-GS deforms a source 3DGS model toward a target shape in any of five input formats, using a learned deformation cage and Jacobian-based Gaussian covariance updates to preserve texture.
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ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior
A drag-driven 3DGS editing method that applies as-rigid-as-possible deformation directly to Gaussian centers and then fine-tunes appearance with a diffusion super-resolution prior.
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