Instant3dit：用于快速编辑3D对象的多视图修复
Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects
AI总结：
本文提出Instant3dit，将3D编辑建模为多视图图像修复，并通过统一diffusion model的微调与多种掩码策略，实现约3秒的高质量3D对象编辑。
摘要：
我们提出了一种生成技术，可在约3秒内编辑以meshes、NeRFs或Gaussian Splats表示的3D形状，无需运行SDS类型的优化。我们的核心洞见是将3D编辑转化为多视图图像修复问题，因为该表示具有通用性，并可借助现有的Large Reconstruction Models库映射回任意3D表示。我们探索了不同的微调策略，以在同一个diffusion model中同时获得多视图生成和修复能力。特别是，修复掩码的设计是训练修复模型的重要因素，我们提出了若干掩码策略，以模拟用户在3D形状上执行的编辑类型。我们的方法将3D生成式编辑从数小时缩短至数秒，并且与先前工作相比能够产生更高质量的结果。
英文摘要：
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without the need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.
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。
