CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation

2026-07-17

AI Summary

Existing 3D editing methods often rely on category-specific models, limiting their generalizability. This work proposes a category-agnostic framework for versatile 3D shape editing based on Coupled Neural Shapes (CNS), which integrates a global semantic latent code with a 3D neural feature volume to enable diverse editing operations through joint optimization. The approach introduces two key innovations: a KV-cache replacement mechanism and latent feature regularization, which together allow precise control over edited regions while preserving geometric consistency in non-edited areas. Compatible with various 3D generative models, the method achieves state-of-the-art quantitative and qualitative results across multiple benchmarks, significantly enhancing both editing flexibility and shape fidelity.

Abstract

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.

Problem

• category-agnostic
• 3D editing
• neural shape representation
• shape manipulation
• foundation models

Innovation

• category-agnostic editing
• coupled neural shape
• 3D foundation models
• neural feature volume
• region-wise control