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Related Experiment Videos

Orchestrating Generative AI Paradigms With Human-in-the-Loop for 3D Generation.

Emanuele Balloni, Lorenzo Stacchio, Marina Paolanti

    IEEE Transactions on Visualization and Computer Graphics
    |May 22, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Imagin3D enhances 3D content creation with human-in-the-loop (HITL) AI, improving user control and accuracy. This system allows iterative refinement of generated 3D models, bridging the gap between AI capabilities and user intent for better 3D generation.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Computer Graphics

    Background:

    • Generative AI is transforming 3D content creation.
    • Existing text-to-3D and image-to-3D methods lack precise user control and refinement capabilities.
    • This leads to generated 3D models that often deviate from user expectations.

    Purpose of the Study:

    • To introduce Imagin3D, a novel human-in-the-loop (HITL) system for controllable 3D content generation.
    • To enhance the adaptability and precision of AI-driven 3D model creation.
    • To enable users to effectively co-create and refine 3D assets.

    Main Methods:

    • Integration of Multimodal Large Language Models (MLLMs) for enhanced control.
    • Utilizing a Multi-View Question Answering module for consistency evaluation.
    • Employing guided inpainting for iterative refinement and Neural Rendering for final asset synthesis.

    Main Results:

    • Imagin3D demonstrates significant improvements in usability, accuracy, and user satisfaction.
    • The system effectively preserves multi-view consistency during iterative refinement.
    • User studies confirm the system's ability to align AI-generated outputs with user intent.

    Conclusions:

    • Human-in-the-loop (HITL) approaches are crucial for bridging the gap between AI generation and user intent in 3D content creation.
    • Imagin3D offers a more accessible and user-centered workflow for interactive 3D generation.
    • The proposed system paves the way for future advancements in controllable generative AI for 3D assets.