发表机构
The Chinese University of Hong Kong; Lingnan University; National University of Singapore; The Hong Kong University of Science and Technology; Autodesk AI Lab; Simon Fraser University（香港中文大学; 岭南大学; 新加坡国立大学; 香港科技大学; 欧特克人工智能实验室; 西蒙弗雷泽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出基于耦合神经形状表示和神经特征体积优化的潜在空间3D形状编辑框架CNS-Edit++，能在特定类别和类别无关模型上实例化，有多种编辑操作符及区域控制机制，经评估其性能优于现有方法。
AI 中文摘要
本文提出了一个基于耦合神经形状（CNS）表示和神经特征体积优化的潜在空间3D形状编辑框架。该工作将基于耦合神经形状优化的CNS-Edit扩展到CNS-Edit++，通过将特定类别的耦合表示推广到使用基础模型的类别无关3D形状编辑。耦合神经形状（CNS）表示将捕获高级形状语义的全局潜在代码与为局部形状操作提供空间上下文的3D神经特征体积耦合。然后制定了一个耦合神经形状优化过程，以根据给定的编辑操作共同优化这两个组件。该框架可以在特定类别的3D反演模型和类别无关的3D基础模型上实例化。提供了各种形状编辑操作符，并引入两种互补的区域控制机制以保留编辑区域外的区域。不同3D生成模型的广泛定量和定性评估证明了该方法优于现有解决方案的强大能力。
英文摘要
This paper presents a latent-space 3D shape editing framework built upon a coupled neural shape (CNS) representation and a neural feature volume optimization. This work extends CNS-Edit, built on Coupled Neural Shape optimization, to CNS-Edit++, by generalizing the category-specific coupled representation to category-agnostic 3D shape editing with foundation models. The Coupled Neural Shape (CNS) representation couples a global latent code that captures high-level shape semantics with a 3D neural feature volume that provides spatial context for local shape manipulation. Then we formulate a coupled neural shape optimization procedure that co-optimizes these two components subject to a given editing operation. Our framework can be instantiated on both the category-specific 3D inversion model and category-agnostic 3D foundation models. We provide various shape editing operators, including copy, resize, delete, mix, point-wise drag, and region-wise drag, each of which is formulated as an objective to guide the CNS optimization. To preserve regions outside the editing area, we further introduce two complementary region-wise control mechanisms, i.e., KV-cache replacement and latent feature regularization. Extensive quantitative and qualitative evaluations across different 3D generative models demonstrate the strong capabilities of our approach over state-of-the-art solutions.
