Proposes Spatial Narrative Score (SNS) evaluation for VLMs' camera motion understanding and introduces CaMo model achieving consistent performance on SNS and direct QA.
arXiv preprint arXiv:2506.17545 , year=
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Backward token warping in ViT-based MLLMs enables reliable reasoning from nearby viewpoints by preserving semantic coherence better than pixel-wise warping or fine-tuning baselines.
APEIRIA distills neuro-symbolic 3D reasoning programs into 3D MLLMs through a curriculum that transfers stepwise verification patterns to achieve transparent yet flexible spatial reasoning.
citing papers explorer
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CaMo: Camera Motion Grounded Evaluation and Training for Vision-Language Models
Proposes Spatial Narrative Score (SNS) evaluation for VLMs' camera motion understanding and introduces CaMo model achieving consistent performance on SNS and direct QA.
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Token Warping Helps MLLMs Look from Nearby Viewpoints
Backward token warping in ViT-based MLLMs enables reliable reasoning from nearby viewpoints by preserving semantic coherence better than pixel-wise warping or fine-tuning baselines.
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Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs
APEIRIA distills neuro-symbolic 3D reasoning programs into 3D MLLMs through a curriculum that transfers stepwise verification patterns to achieve transparent yet flexible spatial reasoning.