PartInteractor is introduced, a representation-to-interaction framework that investigates how semantic part hierarchies can be incorporated into generative XR authoring, and exposed as first-class, directly manipulable units, turning one-shot prompt-to-object generation into continuous component-level co-creation.
Abstract
As Extended Reality (XR) evolves into an immersive computing medium, interactive 3D authoring becomes essential for creative and functional workflows. However, existing generative XR systems produce monolithic outputs lacking explicit semantic structure, limiting post-generation control. We introduce PartInteractor, a representation-to-interaction framework that investigates how semantic part hierarchies can be incorporated into generative XR authoring, and exposed as first-class, directly manipulable units, turning one-shot prompt-to-object generation into continuous component-level co-creation. PartInteractor supports speech, sketch, and image inputs, integrating an LLM interpreter with a retrieval-generation strategy to scaffold user intent prior to 3D generation. Instead of producing monolithic objects, our system generates semantically decomposed 3D assets with explicit part hierarchies, enabling rich component-level interaction over object structure and composition. Our evaluations suggest that part-aware representation increases post-generation control and reduces reliance on whole-object regeneration, while intent scaffolding mitigates ambiguity and improves intent-result alignment, together supporting more expressive and controllable human-AI co-creation workflows. These results highlight part-aware representation and intent scaffolding as promising design considerations for future generative XR authoring systems.
Experiments demonstrate that the MultiCube method can generate high-quality compositional 3D objects with precise part-level control, including those with unique layouts difficult to achieve with text or image prompting alone.
Ava Pun, Kang-Le Deng, Yi-Heng Zhu et al.· 0 citations
SCULPT iteratively applies a joint split predictor to generate one extracted part together with the remaining object, and demonstrates state-of-the-art geometry on PartObjaverse while preserving strong complete-object reconstruction after part assembly.
Sikuang Li, Chen Yang, Jie-Min Fang et al.· 0 citations
3D scene generation has rapidly evolved, significantly promoting the innovation of content creation. In this context, interaction techniques serve as a pivotal bridge connecting user intent with the generative models, thereby enabling precise control, real-time feedback and personalized customization of complex 3D scen...
Yuqi Li, Si-Wei Meng, Chuan-Guang Yang et al.· Proceedings of the Thirty-Fi...· 33 citations· ⚡1
Results demonstrate that immersive WebXR representations substantially strengthen user engagement and understanding, offering a scalable pathway for employer branding and digital recruitment, and point to interactivity as a key avenue for future work.
Louis Burk, Christoph Scharnagl, Uwe Wienkop· International Conference on...· 0 citations
Generative AI lets anyone create rich visual content in seconds, yet translating that content into a physically fabricable artifact still demands manual decomposition, occlusion repair, and structural verification that most tools leave entirely to the user. We present FabDreamer, an image-to-physical system that carrie...
Chen-Feng Gao, Zeya Chen, An Yang et al.· 0 citations
DesignAgent3D is presented, an interactive multimodal agentic framework that reformulates 3D scene editing as a designer-like Plan-Perceive-Act paradigm, delivering superior semantic intent alignment, impeccable spatial localization accuracy, and high-fidelity multi-view consistency.
Xiu-Jin Liu, Tian-Yu Yang, Yilun Zhao et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.