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Explainable generative 3D design driven by social media semantics
Zhenyu Wang1, Karen Sato2, Jianmin Wang3,4,5
1College of Design and Innovation, Tongji University, Shanghai, PR China.
Nature Communications
|June 23, 2026
Summary
Social media semantics, often seen as noise, contain stable patterns useful for 3D generation. These patterns improve structural quality and semantic consistency in generative models.
Area of Science:
- Computer Science
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Social media data is rich in user preferences but difficult to use for generative tasks due to unstructured semantics.
- Previous approaches treated social media semantics as noise, hindering their application in generative design.
Purpose of the Study:
- To demonstrate that stable, interpretable patterns exist within social media semantics.
- To show these patterns can be systematically abstracted and validated for 3D generation.
- To establish a paradigm for socially informed generative 3D design.
Main Methods:
- Utilized 60,500 real-world social media posts for analysis.
- Developed CoX-3D, an explainable generative framework with an Explainable Mediation Layer.
- Bridged social computing (macroscopic) with physical rendering (microscopic) using semantic patterns.
Main Results:
- Reduced structural reconstruction error by over 50% through incorporating stable semantic patterns.
- Improved semantic consistency (CLIP similarity) by 13.1%.
- Semantic activation and attribution analyses revealed distinct roles of semantic factors in geometric and stylistic decisions.
Conclusions:
- Social media semantics are a systematically modelable and interpretable cognitive resource.
- Stable semantic patterns from social media can significantly enhance 3D generation quality and consistency.
- This research establishes a generalizable method for leveraging social data in generative 3D design.
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