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

• URL: http://arxiv.org/abs/2607.16577v2
• Date: Tue, 21 Jul 2026 00:59:33 GMT
• Title: CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation
• Abstract summary: We present a latent-space 3D shape editing framework built upon a coupled neural shape representation and a neural feature volume optimization.<n>Our framework can be instantiated on both the category-specific 3D inversion model and category-agnostic 3D foundation models.
• Score: 50.85783299438816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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