Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

Researchers introduce GeoVR, a framework that enhances multimodal large language models with 3D spatial awareness by learning geometric representations from 2D video sequences. Using four complementary geometric targets including camera pose estimation, depth mapping, and 3D feature distillation, the approach achieves state-of-the-art performance on spatial reasoning benchmarks without requiring large-scale 3D training data.

GeoVR addresses a fundamental limitation in current multimodal large language models: their inability to maintain spatial and geometric consistency across sequential frames despite their strong 2D semantic understanding. By leveraging purely 2D video data, the framework bypasses the scarcity challenge of large-scale 3D datasets while injecting genuine 3D awareness into model representations. The technical approach demonstrates sophistication through its multi-objective learning strategy that simultaneously handles inter-frame camera dynamics, physical distance calibration via depth maps, metric scale factors, and multi-scale 3D feature alignment. This represents a meaningful advancement beyond superficial feature fusion techniques.

The research emerges from a broader recognition that foundation models require explicit geometric grounding to perform spatial reasoning tasks effectively. Current MLLMs, while powerful at language and 2D visual tasks, fail when confronted with problems demanding understanding of three-dimensional relationships and perspectives. GeoVR's achievement of state-of-the-art performance on spatial reasoning benchmarks validates the hypothesis that geometric knowledge distillation from pre-trained 3D models, applied as training constraints on 2D video data, successfully restructures internal semantic representations.

The implications extend beyond academic interest. Applications in robotics, autonomous systems, augmented reality, and 3D content understanding would benefit significantly from MLLMs with native spatial intelligence. The framework's efficiency in avoiding large-scale 3D data requirements makes the approach scalable and practical for broader implementation. Future development likely involves expanding these spatial reasoning capabilities across more complex multi-frame scenarios and real-world applications.

• →GeoVR enables MLLMs to learn genuine 3D spatial awareness using only 2D video sequences without requiring large-scale 3D datasets.
• →The framework employs four geometric objectives: camera pose estimation, depth regression, metric scale prediction, and 3D feature distillation.
• →State-of-the-art performance on spatial reasoning benchmarks demonstrates that geometric constraints successfully reshape MLLM internal representations.
• →The approach addresses critical limitations in current foundation models' ability to maintain spatial consistency across video frames.
• →Potential applications span robotics, autonomous systems, and AR/VR contexts where spatial understanding is essential.