Easy3E: 通过校正体素流实现的前馈3D资产编辑
Easy3E: Feed-Forward 3D Asset Editing via Rectified Voxel Flow
AI总结：
本文提出基于TRELLIS生成框架的高效前馈3D编辑方法，通过单视角编辑实现3D模型修改，解决训练自由2D编辑适应结构化3D表示及压缩3D特征外观保真度瓶颈问题。
摘要：
现有3D编辑方法依赖计算密集型场景级迭代优化，存在多视图不一致问题。我们提出基于TRELLIS生成框架的有效前馈3D编辑框架，能够从单个编辑视角修改3D模型。我们的框架解决两个关键问题：将训练自由的2D编辑适应结构化3D表示，以及克服压缩3D特征中的外观保真度瓶颈。为确保几何一致性，我们引入Voxel FlowEdit，一种在稀疏体素潜在空间中的编辑驱动流，能够在单次通过中实现全局一致的3D变形。为恢复高保真细节，我们开发了基于法线的单到多视图生成模块作为外部外观先验，成功恢复高频纹理。实验表明，我们的方法实现了快速、全局一致且高保真的3D模型编辑。
英文摘要：
Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and feed-forward 3D editing framework based on the TRELLIS generative backbone, capable of modifying 3D models from a single editing view. Our framework addresses two key issues: adapting training-free 2D editing to structured 3D representations, and overcoming the bottleneck of appearance fidelity in compressed 3D features. To ensure geometric consistency, we introduce Voxel FlowEdit, an edit-driven flow in the sparse voxel latent space that achieves globally consistent 3D deformation in a single pass. To restore high-fidelity details, we develop a normal-guided single to multi-view generation module as an external appearance prior, successfully recovering high-frequency textures. Experiments demonstrate that our method enables fast, globally consistent, and high-fidelity 3D model editing.
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。
