Copy-Trasform-Paste: Zero-Shot Object-Object Alignment Guided by Vision-Language and Geometric Constraints

• URL: http://arxiv.org/abs/2601.14207v1
• Date: Tue, 20 Jan 2026 18:12:55 GMT
• Title: Copy-Trasform-Paste: Zero-Shot Object-Object Alignment Guided by Vision-Language and Geometric Constraints
• Authors: Rotem Gatenyo, Ohad Fried,
• Abstract summary: We study zero-shot 3D alignment of two given meshes, using a text prompt describing their relation.<n>We optimize the relative pose at test time, updating translation, rotation, and isotropic scale with CLIP-driven gradients.<n>Our method outperforms all alternatives, yielding semantically faithful and physically plausible alignments.
• Score: 12.704390013489054
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We study zero-shot 3D alignment of two given meshes, using a text prompt describing their spatial relation -- an essential capability for content creation and scene assembly. Earlier approaches primarily rely on geometric alignment procedures, while recent work leverages pretrained 2D diffusion models to model language-conditioned object-object spatial relationships. In contrast, we directly optimize the relative pose at test time, updating translation, rotation, and isotropic scale with CLIP-driven gradients via a differentiable renderer, without training a new model. Our framework augments language supervision with geometry-aware objectives: a variant of soft-Iterative Closest Point (ICP) term to encourage surface attachment and a penetration loss to discourage interpenetration. A phased schedule strengthens contact constraints over time, and camera control concentrates the optimization on the interaction region. To enable evaluation, we curate a benchmark containing diverse categories and relations, and compare against baselines. Our method outperforms all alternatives, yielding semantically faithful and physically plausible alignments.

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