Explainable generative 3D design driven by social media semantics

Although social media extensively captures user preferences and aesthetic intentions, its highly unstructured and emotionally expressive semantics have long been regarded as noise that is difficult to translate into reliable generative cues. Here we show that social media semantics contain stable patterns that can be systematically abstracted and validated, and that these patterns exert interpretable effects on the structural quality and semantic consistency of three-dimensional (3D) generation. Using 60,500 real-world social media posts, we validate this finding through CoX-3D, an explainable generative framework that introduces an Explainable Mediation Layer to bridge macroscopic social computing with microscopic physical rendering. Incorporating these stable semantic patterns reduces structural reconstruction error by over 50% and improves semantic consistency, measured by CLIP similarity, by 13.1%. Semantic activation and attribution analyses further reveal differentiated roles of semantic factors in geometric and stylistic decisions. These results demonstrate that social media semantics constitute a systematically modelable and interpretable cognitive resource, establishing a generalizable paradigm for socially informed generative 3D design. This study is on socially informed generative 3D design. Here, authors developed CoX3D, an explainable framework linking social media semantics to 3D generation, showing improved structural accuracy, semantic consistency, and interpretable design effects.