Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences. This approach effectively restructures the semantic latent space within MLLMs to unlock spatial intelligence. Rather than employing superficial feature mixing, GeoVR reshapes the internal representations of the MLLM by distilling geometry knowledge from pre-trained 3D foundation models. This is accomplished through a multi-objecti...
| Authors | Haibo Wang, Lifu Huang |
|---|---|
| Primary category | cs.CV |
| Published | 4 June 2026 |
| Last updated at source | 18 June 2026 |
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