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arXiv · 2608.18560

SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects

Ru Wang·Rahul Jain·Koichiro Niinuma·Aakar Gupta
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Abstract

Fine-grained control over continuous semantic attributes of 3D objects is essential for 3D content creation, but is not well supported by conventional 3D modeling workflows or prompt-based interaction with existing generative AI tools. While slider-based methods have proven effective for fine-grained semantic control in 2D image generation, no equivalent approach exists for 3D. Extending these 2D methods to 3D is non-trivial due to challenges unique to 3D, including geometric integrity and cross-view coherence. We present SemanticSlider3D, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training. Given a user-specified attribute, our pipeline constructs a semantic editing direction in the latent space of a state-of-the-art 3D generation model, presenting a diverse and coherent spectrum of 3D variations. A technical validation on a dataset of 50 3D object-attribute pairs shows our method was preferred by all five human assessors across variation range, consistency, 3D object quality, and attribute disentanglement, over a baseline combining a 2D slider with an image-to-3D model. An exploratory study with six participants demonstrates that SemanticSlider3D supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.

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Ru Wang, Rahul Jain, Koichiro Niinuma, Aakar Gupta. 2026-08-29. SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects. https://arxiv.org/abs/2608.18560

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