ArtisanGS: 带有AI和人机协作的高斯点选择交互工具
ArtisanGS: Interactive Tools for Gaussian Splat Selection with AI and Human in the Loop
AI总结：
ArtisanGS通过交互式工具实现高斯点选择与分割，结合AI与人工干预，提供灵活的局部编辑功能，适用于真实场景中的可控编辑。
摘要：
在3D高斯点（3DGS）的表示家族中，已经成为传统图形在越来越多的应用中可行的替代方案，包括最近的技术，这些技术有助于物理模拟和动画。然而，从真实场景中提取可用的对象仍然具有挑战性，且针对这种表示的可控编辑技术有限。与大多数新兴技术不同，这些技术专注于自动解决方案或高级编辑，我们引入了一套围绕多功能高斯点选择和分割的交互式工具。我们提出了一种快速的AI驱动方法，将用户引导的2D选择掩码传播到3DGS选择。这种方法允许在出现错误时进行用户干预，并进一步与灵活的手动选择和分割工具结合。这些工具允许用户在无序的3DGS场景中实现任何二进制分割。我们评估了我们的工具集与3DGS选择的最先进方法，并通过开发一个用户引导的局部编辑方法来证明其在后续应用中的用途，利用定制的视频扩散模型。通过灵活的选择工具，用户可以直接控制AI可以修改的区域。我们的选择和编辑工具可以用于任何真实场景，无需额外优化。
英文摘要：
Representation in the family of 3D Gaussian Splats (3DGS) are growing into a viable alternative to traditional graphics for an expanding number of application, including recent techniques that facilitate physics simulation and animation. However, extracting usable objects from in-the-wild captures remains challenging and controllable editing techniques for this representation are limited. Unlike the bulk of emerging techniques, focused on automatic solutions or high-level editing, we introduce an interactive suite of tools centered around versatile Gaussian Splat selection and segmentation. We propose a fast AI-driven method to propagate user-guided 2D selection masks to 3DGS selections. This technique allows for user intervention in the case of errors and is further coupled with flexible manual selection and segmentation tools. These allow a user to achieve virtually any binary segmentation of an unstructured 3DGS scene. We evaluate our toolset against the state-of-the-art for Gaussian Splat selection and demonstrate their utility for downstream applications by developing a user-guided local editing approach, leveraging a custom Video Diffusion Model. With flexible selection tools, users have direct control over the areas that the AI can modify. Our selection and editing tools can be used for any in-the-wild capture without additional optimization.
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。
