ShapeFusion：一种用于局部形状编辑的3D扩散模型
ShapeFusion: A 3D diffusion model for localized shape editing
AI总结：
针对PCA参数化3D模型难以局部、解耦编辑的问题，本文提出ShapeFusion扩散模型及掩码训练策略，支持用户自定义区域和顶点句柄，在保留未编辑区域的同时提升局部性、多样性、可解释性和推理速度。
摘要：
在3D计算机视觉领域，参数化模型已成为创建逼真且富有表现力的3D虚拟化身的突破性方法。传统上，这些模型依赖主成分分析（PCA），因为它能够将数据分解到一个可最大程度捕捉形状变化的正交空间中。然而，由于正交性约束以及PCA分解的全局性，这些模型难以对3D形状进行局部和解耦编辑，这严重影响了它们在面部雕刻等需要精细控制的应用中的使用。本文利用扩散模型在3D网格上实现多样化且完全局部化的编辑，同时完整保留未编辑区域。我们提出了一种有效的扩散掩码训练策略，该策略在设计上便于对任意形状区域进行局部操作，而不受限于预定义区域或稀疏的预定义控制顶点集合。按照我们的框架，用户可以显式设置其选择的操作区域，并定义任意顶点集合作为句柄来编辑3D网格。与当前最先进方法相比，我们的方法比依赖潜代码状态的方法带来更具可解释性的形状操作、更强的局部性和生成多样性，同时比基于优化的方法提供更快的推理。项目页面：https://rolpotamias.github.io/Shapefusion/
英文摘要：
In the realm of 3D computer vision, parametric models have emerged as a ground-breaking methodology for the creation of realistic and expressive 3D avatars. Traditionally, they rely on Principal Component Analysis (PCA), given its ability to decompose data to an orthonormal space that maximally captures shape variations. However, due to the orthogonality constraints and the global nature of PCA's decomposition, these models struggle to perform localized and disentangled editing of 3D shapes, which severely affects their use in applications requiring fine control such as face sculpting. In this paper, we leverage diffusion models to enable diverse and fully localized edits on 3D meshes, while completely preserving the un-edited regions. We propose an effective diffusion masking training strategy that, by design, facilitates localized manipulation of any shape region, without being limited to predefined regions or to sparse sets of predefined control vertices. Following our framework, a user can explicitly set their manipulation region of choice and define an arbitrary set of vertices as handles to edit a 3D mesh. Compared to the current state-of-the-art our method leads to more interpretable shape manipulations than methods relying on latent code state, greater localization and generation diversity while offering faster inference than optimization based approaches. Project page: https://rolpotamias.github.io/Shapefusion/
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。
