SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts, which often fall short in geometric specificity: language can be ambiguous, and images are cumbersome to edit. In this work, we introduce SpaceControl, a training-free test-time method for explicit spatial control of 3D generation. Our approach accepts a wide range of geometric inputs, from coarse primitives to detailed meshes, and integrates seamlessly with modern pre-trained generative models without requiring any additional training. A controllable parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SpaceControl outperforms both training-based and optimization-based baselines in geometric faithfulness while preserving high visual quality. Finally, we present an interactive user interface that enables online editing of superquadrics for direct conversion into textured 3D assets, facilitating practical deployment in creative workflows. Find our project page at https://spacecontrol3d.github.io/
693643b03962c926cf6807c6
Elisabetta Fedele
claimed_verified
2025-12-08T08:29:57.199Z
62df0ae635a8bfe88fc4ac80
https://cdn-avatars.huggingface.co/v1/production/uploads/62df0ae635a8bfe88fc4ac80/yWu5Kfq_88Ni2uy3HvnLe.jpeg
elisabetta fedele
efedele
user
efedele
693643b03962c926cf6807c7
Francis Engelmann
693643b03962c926cf6807c8
Ian Huang
693643b03962c926cf6807c9
Or Litany
693643b03962c926cf6807ca
Marc Pollefeys
693643b03962c926cf6807cb
Leonidas Guibas
SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling
Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts, which often fall short in geometric specificity: language can be ambiguous, and images are cumbersome to edit. In this work, we introduce SpaceControl, a training-free test-time method for explicit spatial control of 3D generation. Our approach accepts a wide range of geometric inputs, from coarse primitives to detailed meshes, and integrates seamlessly with modern pre-trained generative models without requiring any additional training. A controllable parameter lets users trade off between geometric fidelity and output realism. Extensive quantitative evaluation and user studies demonstrate that SpaceControl outperforms both training-based and optimization-based baselines in geometric faithfulness while preserving high visual quality. Finally, we present an interactive user interface that enables online editing of superquadrics for direct conversion into textured 3D assets, facilitating practical deployment in creative workflows. Find our project page at https://spacecontrol3d.github.io/
SpaceControl enables explicit spatial control of 3D generation using various geometric inputs, outperforming existing methods in geometric faithfulness while maintaining visual quality.
3D assets generative methods text prompts image prompts SpaceControl geometric inputs primitives meshes pre-trained generative models geometric fidelity visual quality superquadrics interactive user interface