ProEdit：简单渐进式方法即可实现高质量3D场景编辑
ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing
AI总结：
ProEdit提出一种简单而有效的渐进式3D场景编辑框架，通过分解子任务控制扩散模型的可行输出空间，减少多视图不一致，实现高质量编辑。
摘要：
本文提出ProEdit——一种简单而有效的框架，通过新颖的渐进式扩散蒸馏引导实现高质量3D场景编辑。受关键观察启发：场景编辑中的多视图不一致源于扩散模型巨大的可行输出空间（FOS），我们的框架通过将整体编辑任务分解为若干子任务，并在场景上逐步执行这些子任务，从而控制FOS的大小并减少不一致性。在该框架内，我们设计了一个难度感知的子任务分解调度器和一个自适应3D高斯溅射（3DGS）训练策略，确保执行每个子任务时的高质量和效率。大量评估表明，我们的ProEdit在各种场景和具有挑战性的编辑任务中均取得了最先进的结果，且仅通过简单框架实现，无需任何昂贵或复杂的附加组件，如蒸馏损失、组件或训练程序。值得注意的是，ProEdit还提供了一种在编辑过程中控制、预览和选择编辑操作“激进程度”的新方法。
英文摘要：
This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene editing is rooted in the diffusion model's large feasible output space (FOS), our framework controls the size of FOS and reduces inconsistency by decomposing the overall editing task into several subtasks, which are then executed progressively on the scene. Within this framework, we design a difficulty-aware subtask decomposition scheduler and an adaptive 3D Gaussian splatting (3DGS) training strategy, ensuring high quality and efficiency in performing each subtask. Extensive evaluation shows that our ProEdit achieves state-of-the-art results in various scenes and challenging editing tasks, all through a simple framework without any expensive or sophisticated add-ons like distillation losses, components, or training procedures. Notably, ProEdit also provides a new way to control, preview, and select the "aggressivity" of editing operation during the editing process.
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。
