arxiv: 2605.07894 · v1 · submitted 2026-05-08 · 💻 cs.HC

Recognition: no theorem link

SpatialPrompt: XR-Based Spatial Intent Expression as Executable Constraints for AI Generative 3D Design

Gavin Johnson, Jymon Ross, Mandy Lui, Qiao Jin, Qiaoran Wang, Wanru Li, Yichen Andy Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-11 03:30 UTC · model grok-4.3

classification 💻 cs.HC

keywords spatial interfacesXR design3D generationexecutable constraintsvoice promptscollaborative creationintent expression

0 comments

The pith

Spatial sketches in XR become executable constraints that guide and refine AI-generated 3D models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a system that lets users express three-dimensional design ideas by drawing rough structures with a virtual pen and adding spoken details about style or semantics. These inputs are converted into rules that an AI generator must follow, supporting repeated adjustments and simultaneous work by multiple people in one shared virtual space where each person's contributions appear in distinct colors. The approach aims to make 3D creation more direct than text-only prompts by preserving spatial relationships and enabling real-time collaboration. An initial check with users indicates the steps feel straightforward and help teams align on intent, while pointing to the value of quicker output and clearer system responses during use.

Core claim

SpatialPrompt shows that rough spatial sketches combined with voice prompts can be turned into executable constraints for controllable 3D generation, allowing iterative refinement and synchronous co-creation where color-coded contributions make individual inputs visible to all participants in the shared space.

What carries the argument

The mapping of 3D pen drawings and voice inputs into executable constraints that direct the generative process while preserving spatial structure and enabling multi-user attribution through color coding.

If this is right

Designers can adjust generated models by editing the original spatial sketch or voice description rather than rewriting full prompts.
Multiple creators can work at the same time in one virtual space with automatic visibility of who contributed which element.
The system supports refinement loops where earlier spatial intent remains active as new constraints are added.
Generation speed and feedback clarity become the main practical bottlenecks once the core constraint mechanism is in place.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The constraint-based approach could transfer to domains such as architectural layout or mechanical part design where rough spatial marks carry more meaning than words alone.
Adding direct editing of the generated constraints themselves might increase precision without losing the initial sketching ease.
Longer-term use with professional teams would test whether the color-coded contributions scale to larger groups or more complex projects.

Load-bearing premise

The assumption that a heuristic evaluation can reliably confirm the workflow feels intuitive and supports shared understanding among collaborators.

What would settle it

A follow-up study in which participants repeatedly fail to produce 3D outputs matching their stated spatial and verbal intent, or in which collaborative sessions show no measurable improvement in shared understanding compared with text-prompt methods.

Figures

Figures reproduced from arXiv: 2605.07894 by Gavin Johnson, Jymon Ross, Mandy Lui, Qiao Jin, Qiaoran Wang, Wanru Li, Yichen Andy Yu.

**Figure 1.** Figure 1: Overview of SpatialPrompt, where users sketch spatial structures with a 3D pen and provide voice prompts in XR to generate and iteratively refine corresponding 3D assets in a tabletop augmented reality workflow. Abstract We present SpatialPrompt, an Extended Reality(XR) system that turns spatial sketches into executable constraints for controllable 3D generation. Users draw rough structures with a 3D pen a… view at source ↗

**Figure 2.** Figure 2: End-to-end workflow of SpatialPrompt: users create a spatial structure model in XR, which is compiled into executable constraints to condition a 3D generation backend, and the generated asset is displayed and refined in XR. externalize ideas in 3D space during early ideation and prototyping [7, 9]. While effective for expressing form and proportion, turning sketches into production-ready assets still ty… view at source ↗

read the original abstract

We present SpatialPrompt, an Extended Reality(XR) system that turns spatial sketches into executable constraints for controllable 3D generation. Users draw rough structures with a 3D pen and add voice prompts for semantic and stylistic intent. The system supports iterative refinement and synchronous co-creation in shared space with color-coded contributions. Implemented on Apple Vision Pro with Logitech Muse and Meshy, a heuristic evaluation suggests that the workflow is intuitive and supports shared understanding in collaborative creation, while revealing needs for faster generation and clearer feedback.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SpatialPrompt builds a workable XR workflow for turning 3D sketches and voice into constraints for AI 3D generation, but the heuristic evaluation is too thin to back the intuitiveness and collaboration claims.

read the letter

The main point is a system that lets users sketch rough 3D structures with a pen on the Vision Pro, layer in voice prompts for style and semantics, and feed the result as constraints to Meshy for model generation, with color-coded support for multiple people working in the same space at once. It also includes iterative refinement loops. The implementation runs on actual hardware with the Logitech Muse pen rather than a simulated setup, which gives the description some grounding. The workflow for converting spatial input into executable constraints is laid out clearly enough that a reader can see how the pieces fit together. This is new as an integrated package even if the individual parts draw from prior XR and generative AI work. It does a decent job showing a practical path for making generative 3D tools more controllable through direct spatial expression. The collaboration angle with shared space and color coding is a reasonable addition for design teams. The soft spot is the evaluation. The paper reports a heuristic assessment that the interface feels intuitive and aids shared understanding, yet it gives no participant numbers, task details, success rates on matching intended outputs, or comparison points. Heuristic reviews can flag obvious problems, but they leave the central claims about controllability and collaboration on shaky ground without more data. Readers working on XR interfaces for creative AI or human-AI co-design would find the system description useful as a concrete example and as a source of open issues like generation speed and feedback clarity. It is not a foundational result, but the integration is specific enough to be worth discussing in that community. I would send this to peer review. The core system idea is coherent and timely, though the authors need to expand the evaluation with quantitative measures and clearer methodology to make the usability arguments hold up.

Referee Report

1 major / 0 minor

Summary. The paper presents SpatialPrompt, an XR system implemented on Apple Vision Pro with Logitech Muse and Meshy that converts spatial sketches (drawn with a 3D pen) and voice prompts into executable constraints for controllable AI generative 3D design. It supports iterative refinement and synchronous collaborative co-creation via color-coded contributions in shared space. A heuristic evaluation is reported to suggest that the workflow is intuitive and promotes shared understanding, while highlighting needs for faster generation and clearer feedback.

Significance. If the central claims hold, this work contributes to HCI by demonstrating a practical integration of spatial intent expression with generative AI in XR, addressing controllability in 3D design and supporting collaborative workflows. The concrete implementation on current hardware provides a useful existence proof for executable-constraint approaches to bridging sketching and AI output.

major comments (1)

[Evaluation section] The heuristic evaluation (described at a high level in the abstract and Evaluation section) provides the sole empirical support for the claims that the workflow is intuitive and supports shared understanding in collaborative creation. However, it reports no details on evaluator count, protocol, specific heuristics used, inter-rater agreement, or quantitative measures of controllability (e.g., success rate of spatial inputs producing intended 3D outputs from the Meshy generator). This leaves the central usability and collaboration assertions without sufficient evidential grounding.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the constructive review of our manuscript. We address the major comment on the Evaluation section below and will revise the paper accordingly to strengthen the reporting of our heuristic evaluation while appropriately scoping our claims.

read point-by-point responses

Referee: [Evaluation section] The heuristic evaluation (described at a high level in the abstract and Evaluation section) provides the sole empirical support for the claims that the workflow is intuitive and supports shared understanding in collaborative creation. However, it reports no details on evaluator count, protocol, specific heuristics used, inter-rater agreement, or quantitative measures of controllability (e.g., success rate of spatial inputs producing intended 3D outputs from the Meshy generator). This leaves the central usability and collaboration assertions without sufficient evidential grounding.

Authors: We agree that the current Evaluation section is high-level and would benefit from greater detail to support the claims. In the revised manuscript, we will expand this section to describe the heuristic evaluation process more fully, including the number of evaluators, the protocol followed, the specific heuristics used (adapted from established sets for XR and collaborative design), and any inter-rater agreement observations. We will also incorporate direct quotes from evaluator feedback to illustrate support for intuitiveness and shared understanding. However, the evaluation was conducted as a heuristic review rather than a controlled experiment, so quantitative metrics such as success rates for spatial-to-3D generation outcomes were not collected. We will revise the abstract and relevant claims to reflect this scope, positioning the evaluation as identifying usability insights and areas for improvement rather than providing statistical validation of controllability. revision: yes

Circularity Check

0 steps flagged

No circularity; descriptive systems paper with no derivations or self-referential reductions

full rationale

The paper is a systems description of an XR workflow implemented on Apple Vision Pro with Logitech Muse and Meshy, using spatial sketches and voice prompts converted to constraints for 3D generation, plus iterative co-creation. It reports a heuristic evaluation suggesting intuitiveness and shared understanding. No equations, fitted parameters, predictions, or mathematical derivations appear in the provided text or abstract. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The evaluation is presented as suggestive evidence rather than a derived result that reduces to inputs by construction. This matches the default non-circular case for non-mathematical HCI/systems papers; the skeptic critique concerns evidence strength, not circularity in any derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a systems description with no mathematical modeling, free parameters, axioms, or invented entities. The central claim rests on the described implementation and heuristic evaluation.

pith-pipeline@v0.9.0 · 5398 in / 1065 out tokens · 52534 ms · 2026-05-11T03:30:19.810889+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 2 internal anchors

[1]

Bakk et al

Ágnes K. Bakk et al. 2025. Applying co-design in social VR.International Journal of Human–Computer Interaction(2025). https://www.tandfonline.com/doi/full/ 10.1080/15710882.2025.2516664

work page doi:10.1080/15710882.2025.2516664 2025
[2]

2007.Sketching User Experiences: Getting the Design Right and the Right Design

Bill Buxton. 2007.Sketching User Experiences: Getting the Design Right and the Right Design. Morgan Kaufmann

work page 2007
[3]

Marina Cidota, Stephan Lukosch, Dragos Datcu, and Heide Lukosch. 2016. Com- paring the Effect of Audio and Visual Notifications on Workspace Awareness using Head-Mounted Displays for Remote Collaboration in Augmented Reality. Augmented Human Research1, 1 (2016). doi:10.1007/s41133-016-0003-x

work page doi:10.1007/s41133-016-0003-x 2016
[4]

Tomás Dorta, Stéphane Safin, Sana Boudhraâ, and Emmanuel Beaudry Marchand

work page
[5]

InProceedings of CAADRIA

Co-Designing in Social VR: Process awareness and suitable representations to empower user participation. InProceedings of CAADRIA. https://arxiv.org/ abs/1906.11004

work page arXiv 1906
[6]

Carl Gutwin and Saul Greenberg. 2002. A Descriptive Framework of Workspace Awareness for Real-Time Groupware.Computer Supported Cooperative Work (CSCW)11 (2002), 411–446. doi:10.1023/A:1021271517844

work page doi:10.1023/a:1021271517844 2002
[7]

Chenhan Jiang. 2024. A Survey On Text-to-3D Contents Generation In The Wild. (2024). arXiv:2405.09431 [cs.CV] doi:10.48550/arXiv.2405.09431

work page doi:10.48550/arxiv.2405.09431 2024
[8]

Jamil Joundi, Yves Christiaens, Jo Saldien, Peter Conradie, and Lieven De Marez

work page
[9]

InProceedings of the Design Society: DESIGN Conference

An Explorative Study Towards Using VR Sketching as a Tool for Ideation and Prototyping in Product Design. InProceedings of the Design Society: DESIGN Conference. https://www.cambridge.org/core/journals/proceedings-of- the-design-society-design-conference/article/an-explorative-study-towards- using-vr-sketching-as-a-tool-for-ideation-and-prototyping-in-pro...

work page arXiv
[10]

Heewoo Jun and Alex Nichol. 2023. Shap-E: Generating Conditional 3D Implicit Functions. (2023). arXiv:2305.02463 [cs.CV] doi:10.48550/arXiv.2305.02463

work page doi:10.48550/arxiv.2305.02463 2023
[11]

Keefe, Robert C

Daniel F. Keefe, Robert C. Zeleznik, and David H. Laidlaw. 2007. Drawing on Air: Input Techniques for Controlled 3D Line Illustration.IEEE Transactions on Visualization and Computer Graphics13, 5 (2007), 1067–1081. https://cs.brown. edu/research/pubs/pdfs/2007/Keefe-2007-DOA.pdf

work page 2007
[12]

Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis

work page
[13]

3D Gaussian Splatting for Real-Time Radiance Field Rendering. (2023). arXiv:2308.04079 [cs.GR] doi:10.48550/arXiv.2308.04079

work page doi:10.48550/arxiv.2308.04079 2023
[14]

Maaike Kleinsmann and Rianne Valkenburg. 2008. Barriers and enablers for creating shared understanding in co-design projects.Design Studies29, 4 (2008), 369–386. doi:10.1016/j.destud.2008.03.003

work page doi:10.1016/j.destud.2008.03.003 2008
[15]

Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. 2023. Magic3D: High-Resolution Text-to-3D Content Creation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/ abs/2211.10440

work page arXiv 2023
[16]

Feng-Lin Liu, Hongbo Fu, Yu-Kun Lai, and Lin Gao. 2024. SketchDream: Sketch- based Text-To-3D Generation and Editing.ACM Transactions on Graphics(2024). doi:10.1145/3658120

work page doi:10.1145/3658120 2024
[17]

Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. 2023. Zero-1-to-3: Zero-shot One Image to 3D Object. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). https://arxiv.org/abs/2303.11328

work page arXiv 2023
[18]

Mildenhall, P

Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2020. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. InEuropean Conference on Computer Vision (ECCV). https://arxiv.org/abs/2003.08934

work page arXiv 2020
[19]

Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen

work page
[20]

Point-E: A System for Generating 3D Point Clouds from Complex Prompts. (2022). arXiv:2212.08751 [cs.CV] doi:10.48550/arXiv.2212.08751

work page internal anchor Pith review doi:10.48550/arxiv.2212.08751 2022
[21]

Jakob Nielsen. 1994. Heuristic Evaluation. InUsability Inspection Methods, Jakob Nielsen and Robert L. Mack (Eds.). John Wiley & Sons, Inc., New York, NY, USA, 25–62. https://dl.acm.org/doi/10.5555/189200.189209

work page doi:10.5555/189200.189209 1994
[22]

Landauer

Jakob Nielsen and Thomas K. Landauer. 1993. A Mathematical Model of the Finding of Usability Problems. InProceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems. 206–213. doi:10.1145/169059. 169166

work page doi:10.1145/169059 1993
[23]

DreamFusion: Text-to-3D using 2D Diffusion

Ben Poole, Ajay Jain, Jonathan T. Barron, et al. 2022. DreamFusion: Text-to-3D using 2D Diffusion.arXiv preprint arXiv:2209.14988(2022). https://arxiv.org/abs/ 2209.14988

work page internal anchor Pith review arXiv 2022
[24]

Sutherland

Ivan E. Sutherland. 1963. Sketchpad: A Man-Machine Graphical Communication System.Proceedings of the Spring Joint Computer Conference (AFIPS)(1963). doi:10.1145/1461551.1461591

work page doi:10.1145/1461551.1461591 1963
[25]

Yuqi Tong, Yue Qiu, Ruiyang Li, Shi Qiu, and Pheng-Ann Heng. 2024. MS2Mesh-XR: Multi-modal Sketch-to-Mesh Generation in XR Environments. arXiv:2412.09008 [cs.CV] https://arxiv.org/abs/2412.09008

work page arXiv 2024
[26]

Miller, Jeremy N

Portia Wang, Mark R. Miller, Jeremy N. Bailenson, et al. 2024. Understanding virtual design behaviors: A large-scale analysis of the design process in Virtual Reality.Design Studies(2024). https://vhil.stanford.edu/sites/g/files/sbiybj29011/ files/media/file/design-studies-wang.pdf

work page 2024
[27]

Qiang Zou, Zhihong Tang, Hsi-Yung Feng, Shuming Gao, Chenchu Zhou, and Yusheng Liu. 2022. A review on geometric constraint solving. (2022). arXiv:2202.13795 [cs.CG] https://arxiv.org/abs/2202.13795

work page arXiv 2022