ShapeUP: Scalable Image-Conditioned 3D Editing

• URL: http://arxiv.org/abs/2602.05676v1
• Date: Thu, 05 Feb 2026 13:59:16 GMT
• Title: ShapeUP: Scalable Image-Conditioned 3D Editing
• Authors: Inbar Gat, Dana Cohen-Bar, Guy Levy, Elad Richardson, Daniel Cohen-Or,
• Abstract summary: ShapeUP is a scalable, image-conditioned 3D editing framework.<n>It formulates editing as a supervised latent-to-latent translation within a native 3D representation.<n>Our evaluations demonstrate that ShapeUP consistently outperforms current trained and training-free baselines in both identity preservation and edit fidelity.
• Score: 44.63222737714384
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in 3D foundation models have enabled the generation of high-fidelity assets, yet precise 3D manipulation remains a significant challenge. Existing 3D editing frameworks often face a difficult trade-off between visual controllability, geometric consistency, and scalability. Specifically, optimization-based methods are prohibitively slow, multi-view 2D propagation techniques suffer from visual drift, and training-free latent manipulation methods are inherently bound by frozen priors and cannot directly benefit from scaling. In this work, we present ShapeUP, a scalable, image-conditioned 3D editing framework that formulates editing as a supervised latent-to-latent translation within a native 3D representation. This formulation allows ShapeUP to build on a pretrained 3D foundation model, leveraging its strong generative prior while adapting it to editing through supervised training. In practice, ShapeUP is trained on triplets consisting of a source 3D shape, an edited 2D image, and the corresponding edited 3D shape, and learns a direct mapping using a 3D Diffusion Transformer (DiT). This image-as-prompt approach enables fine-grained visual control over both local and global edits and achieves implicit, mask-free localization, while maintaining strict structural consistency with the original asset. Our extensive evaluations demonstrate that ShapeUP consistently outperforms current trained and training-free baselines in both identity preservation and edit fidelity, offering a robust and scalable paradigm for native 3D content creation.

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