# Canvas3D: Empowering Precise Spatial Control for Image Generation with Constraints from a 3D Virtual Canvas

arXiv: https://arxiv.org/abs/2508.07135 · HTML: https://mlsanity.com/paper/2508.07135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects](https://mlsanity.com/paper/2608.18560/) (2026)

## Abstract (arXiv)

Generative AI (GenAI) has significantly advanced the ease and flexibility of image creation. However, it remains a challenge to precisely control spatial compositions, including object arrangement and scene conditions. To bridge this gap, we propose Canvas3D, an interactive system leveraging a 3D engine to enable precise spatial manipulation for image generation. Upon user prompt, Canvas3D automatically converts textual descriptions into interactive objects within a 3D engine-driven virtual canvas, empowering direct and precise spatial configuration. These user-defined arrangements generate explicit spatial constraints that guide generative models in accurately reflecting user intentions in the resulting images. We conducted a closed-end comparative study between Canvas3D and a baseline system. And an open-ended study to evaluate our system "in the wild". The result indicates that Canvas3D outperforms the baseline on spatial control, interactivity, and overall user experience.