Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models
arXiv:2606.05833v1 Announce Type: cross Abstract: 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 intern
The rapid advancement of Multimodal Large Language Models (MLLMs) has highlighted their limitations in genuinely understanding 3D space, necessitating immediate research into geometric representation learning.
Achieving true spatial intelligence in MLLMs is critical for their application in real-world physical environments, enhancing their ability to interact with and reason about the world beyond semantic labels.
This research suggests a pathway to overcome a fundamental limitation in current MLLMs, enabling more robust and reliable AI systems with intrinsic 3D awareness from readily available 2D video data.
- · AI/ML researchers
- · Robotics industry
- · Virtual/Augmented Reality developers
- · Computer vision companies
- · Companies relying solely on 2D semantic AI
- · Systems with poor spatial understanding
GeoVR could significantly improve the contextual understanding of MLLMs in dynamic environments.
Enhanced spatial intelligence would accelerate the capabilities of embodied AI, including advanced robotics and autonomous systems.
More sophisticated robotic agents, capable of navigating and manipulating complex physical spaces with human-like understanding, could emerge.
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Read at arXiv cs.AI