SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modeling

2025-12-04

AI Summary

Existing 3D generative models struggle with intuitive and precise geometric control: text prompts are often ambiguous, while image-based editing is cumbersome and inefficient. This paper introduces the first training-free, test-time spatial control framework that supports diverse spatial inputs—from simple primitives to complex meshes—and directly injects them into pretrained 3D generative models for explicit geometric guidance during synthesis. Our method leverages differentiable rendering and feature alignment to integrate geometric priors via a spatial conditioning injection mechanism, enabling plug-and-play integration and adjustable trade-offs between geometric fidelity and visual realism. Experiments demonstrate substantial improvements in geometric accuracy over fine-tuning- or optimization-based baselines. User studies confirm superior intuitiveness and editing efficiency. The framework enables real-time interactive editing—from quadric surfaces to textured 3D assets—without model retraining.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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/

Problem

• Provides explicit spatial control for 3D generation
• Uses geometric inputs like primitives or meshes for precision
• Balances geometric fidelity with visual realism in outputs

Innovation

• Test-time spatial control for 3D generation
• Training-free integration with pre-trained models
• Interactive editing of geometric inputs for assets