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AI Research
arXiv CS.AI·3d ago·5 min read
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning
TL;DR
SpatialASM introduces a framework that enables Large Vision-Language Models (LVLMs) to internalize Active Spatial Mentalization (ASM), enhancing their spatial reasoning capabilities. This approach significantly improves performance across seven spatial reasoning benchmarks while maintaining general vision abilities.
✦ Why It Matters
Implement SpatialASM to enhance your LVLMs' spatial reasoning capabilities today.
Key Takeaways
- Explore integrating SpatialASM into your existing LVLMs.
- Utilize explicit ASM trajectories for improved view planning.
- Test performance on spatial reasoning benchmarks to validate improvements.
- Consider the framework's potential for enhancing scene imagination.
How It Works
SpatialASM employs an allocentric novel-view generator that grounds hypothetical viewpoints in visual evidence. It provides explicit ASM trajectories that guide LVLMs on when and how to adaptively invoke view planning during spatial reasoning.
Additionally, it constructs perspective-taking tasks that further enhance the model's ability to imagine scenes from different viewpoints.