arXiv:2505.17015v2 Announce Type: replace-cross Abstract: Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images, leaving them ill-suited for physical-world applications that require multi-frame reasoning. In this paper, we propose a framework to equip MLLMs with multi-frame spatial understanding by integrating fundamental spatial skills, including depth perception, visual correspondence, and dynamic perception. We design a novel data pipeline and collect the MultiSPA dataset of more than 27 million samp
The rapid advancement of MLLMs has highlighted their current limitations in complex spatial reasoning, prompting immediate research into multi-frame understanding to unlock real-world applications.
Improving MLLMs' spatial understanding across multiple frames is critical for enabling truly autonomous AI agents capable of navigating and interacting effectively with the physical world.
MLLMs will no longer be limited to single-image understanding but will gain fundamental spatial skills, allowing them to process and interpret dynamic visual information over time.
- · AI Agent Developers
- · Robotics Industry
- · Computer Vision Researchers
- · Logistics & Automation
- · Companies reliant on primitive visual AI
- · Single-modality AI solutions
Artificial intelligence systems will become more adept at understanding and navigating complex, dynamic physical environments.
This improved spatial understanding will accelerate the development and deployment of advanced robotics and autonomous systems across various industries.
The enhanced capabilities of AI agents in the physical world could lead to significant shifts in labor markets for tasks requiring spatial reasoning and physical interaction.
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