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Spatial Knowledge Graph-Guided Multimodal Synthesis

arXiv — cs.CV•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Spatial Knowledge Graph-Guided Multimodal Synthesis

Recent advancements in Multimodal Large Language Models (MLMs) have significantly improved their overall capabilities; however, spatial perception continues to present a notable challenge (F1, A1). Addressing this issue, a systematic framework for multimodal data synthesis has been proposed to enhance spatial common sense in generated data (F2, A2). This framework aims to integrate spatial knowledge graphs to guide the synthesis process, thereby improving the model's understanding of spatial relationships across different modalities. The approach is designed to overcome limitations in current MLMs by providing structured spatial context during data generation. Such developments are crucial for applications requiring accurate spatial reasoning, including computer vision and robotics. The ongoing research, as documented on arXiv, situates this work within the broader effort to refine multimodal AI systems (connected coverage). Overall, the framework represents a promising step toward resolving persistent spatial perception challenges in multimodal synthesis.

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