Spatial Knowledge Graph-Guided Multimodal Synthesis

Yida Xue, Zhen Bi, Jinnan Yang, Jungang Lou, Huajun Chen, Ningyu Zhang

Spatial Knowledge Graph-Guided Multimodal Synthesis: 4 upvotes on Hugging Face Daily Papers, #57 of 76 papers on 2025-05-28. Day-by-day upvote history.

Recent advances in multimodal large language models (MLLMs) have significantly enhanced their capabilities; however, their spatial perception abilities remain a notable limitation. To address this challenge, multimodal data synthesis offers a promising solution. Yet, ensuring that synthesized data adhere to spatial common sense is a non-trivial task. In this work, we introduce SKG2Data, a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation. SKG2Data automatically constructs a Spatial Knowledge Graph (SKG) to emulate human-like perception of spatial directions and distances, which is subsequently utilized to guide multimodal data synthesis. Extensive experiments demonstrate that data synthesized from diverse types of spatial knowledge, including direction and distance, not only enhance the spatial perception and reasoning abilities of MLLMs but also exhibit strong generalization capabilities. We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence.

Paper page on Hugging Face · arXiv

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