GSEdit：基于高斯溅射的高效文本引导3D对象编辑
GSEdit: Efficient Text-Guided Editing of 3D Objects via Gaussian Splatting
AI总结：
本研究提出基于高斯溅射的文本引导3D编辑流水线GSEdit，借助预训练图像扩散模型与SDS损失优化，在消费级硬件上数分钟即可完成保细节的3D对象风格外观编辑，效率优于类NeRF方法。
摘要：
我们提出了GSEdit，一种基于高斯溅射（Gaussian Splatting）模型的文本引导3D对象编辑流水线。该方法能够在不改变3D对象主要细节的前提下编辑其风格与外观，且在消费级硬件上仅需数分钟即可完成。我们通过利用高斯溅射表示3D场景来解决这一问题，同时借助预训练的基于图像的扩散模型逐步改变图像监督信号，以此优化模型。输入对象可以是3D三角网格，也可以直接由DreamGaussian等生成模型输出的高斯形式提供。GSEdit可确保不同视角间的一致性，维持原始对象信息的完整性。与此前依赖类NeRF MLP模型的方法相比，GSEdit的效率优势突出，大幅提升了3D编辑任务的速度。我们的编辑流程通过应用SDS损失进行优化，确保编辑结果精准准确。全面评估表明，GSEdit能够根据给定文本指令有效改变对象的形状和外观，同时保持其连贯性与细节。
英文摘要：
We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models. Our method enables the editing of the style and appearance of 3D objects without altering their main details, all in a matter of minutes on consumer hardware. We tackle the problem by leveraging Gaussian splatting to represent 3D scenes, and we optimize the model while progressively varying the image supervision by means of a pretrained image-based diffusion model. The input object may be given as a 3D triangular mesh, or directly provided as Gaussians from a generative model such as DreamGaussian. GSEdit ensures consistency across different viewpoints, maintaining the integrity of the original object's information. Compared to previously proposed methods relying on NeRF-like MLP models, GSEdit stands out for its efficiency, making 3D editing tasks much faster. Our editing process is refined via the application of the SDS loss, ensuring that our edits are both precise and accurate. Our comprehensive evaluation demonstrates that GSEdit effectively alters object shape and appearance following the given textual instructions while preserving their coherence and detail.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
