ToW3D：基于GAN的交互式点驱动网格编辑中的一致性感知方法
ToW3D: Consistency-aware Interactive Point-based Mesh Editing on GANs
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出ToW3D，通过拔河式优化实现三维GAN网格编辑，兼顾局部拖拽与全局一致性，提升大变形下的外观保真。
AI中文摘要：
本文提出了ToW3D方法，通过形状变形与外观一致性之间的“拔河”竞争，实现对三维生成对抗网络（GANs）的精确且一致的操控。现有的基于点的GAN编辑方法，如DragGAN和GANWarping，在二维图像操作中已展现出卓越性能。然而，由于三维生成器因训练数据有限而泛化能力弱于二维，在编辑网格局部区域时，这些方法常面临全局外观剧烈变化的问题。为解决此问题，我们设计了一种“局部拖拽，全局推回”的流程，迭代执行两个优化步骤：1）将点拉向目标位置；2）将结构和语义推回至源状态。具体而言，我们设计了基于结构的结构适配模块，确保基本几何属性的保留，以及语义保持模块，维护不同视角间的语义相似性。大量定性和定量实验表明，我们的ToW3D方法在外观一致性和保真度方面优于先前方法，尤其是在大变形场景下。
英文摘要：
In this paper, we propose ToW3D that enables precise and consistent control over 3D generative adversarial networks (GANs) with the Tug-of-War competition between shape deformation and appearance consistency. Existing point-based GAN editing methods such as DragGAN and GANWarping have yielded impressive performance for 2D image manipulation. However, as 3D generators present weaker generalization ability compared with 2D due to limited training data, they would suffer from drastic changes in global appearance when editing local areas of meshes. To address this, we design a pipeline of ``drag locally, shove globally'', which iteratively performs two optimization steps: 1) pull the point towards the target, and 2) push the structure and semantics back to the source. Specifically, we design a structure adaption module based on structure which guarantees the preservation of basic geometric properties, and a semantic preservation module that maintains semantic similarity across different views. Extensive qualitative and quantitative experiments demonstrate superiority of our ToW3D approach over prior methods in terms of appearance consistency and fidelity especially under large deformations.
