Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian Splatting

作者：Yansong Qu, Dian Chen, Xinyang Li, Xiaofan Li, Shengchuan Zhang, Liujuan Cao, Rongrong Ji · 发表于：International Conference on Computer Graphics and Interactive Techniques · 年份：2025 · DOI：10.1145/3721238.3730600 · 被引用次数：31 · 研究领域：Computer Science

Recent advancements in generative models have significantly propelled 3D scene editing. While existing methods excel at text-guided texture modifications for 3D representations like 3D Gaussian Splatting (3DGS), they struggle with geometric transformations (e.g., rotating a character’s head) and lack precise spatial control over edits due to the inherent ambiguity of language-driven guidance. To address these limitations, we introduce DYG, a 3D drag-based editing framework for 3DGS. Users intuitively define editing regions using 3D masks and specify desired transformations through pairs of control points. DYG integrates the implicit triplane representation to establish the geometric scaffold of editing results, effectively overcoming suboptimal editing outcomes caused by the sparsity of 3DGS in the desired editing regions. Additionally, we incorporate a drag-based Latent Diffusion Model through the proposed Drag-SDS loss, enabling flexible, multi-view consistent, and fine-grained editing. Extensive experiments demonstrate that DYG enables effective drag-based editing, outperforming other baselines in terms of editing effect and quality. Additional results are available on our project page: https://quyans.github.io/Drag-Your-Gaussian/.