Paper Overview
Research Area: Computer Vision (CV) Authors: Xianjin Wu, Dingkang Liang, Tianrui Feng Published: 2026-03-19 arXiv: 2503.16932
Abstract (Original)
While Multimodal Large Language Models demonstrate impressive semantic capabilities, they often suffer from spatial blindness, struggling with fine-grained geometric reasoning and physical dynamics. Existing solutions typically rely on explicit 3D modalities or complex geometric scaffolding, which are limited by data scarcity and generalization challenges. In this work, we propose a paradigm shift by leveraging the implicit spatial prior within large-scale video generation models.
Summary
Multimodal Large Language Models (MLLMs), despite their outstanding semantic understanding, often exhibit a "spatial blindness" problem, making it difficult to perform fine-grained geometric reasoning and physical dynamics modeling. This study proposes a paradigm shift that exploits the implicit spatial priors contained in large-scale video generation models. Pretrained video diffusion models are re-purposed as latent world simulators, injecting dense geometric cues into MLLMs without explicit 3D supervision.
Key Ideas
- Problem: MLLMs lack spatial awareness — geometric reasoning and physical dynamics are weak points.
- Limitation of prior work: explicit 3D modalities or geometric scaffolding suffer from data scarcity and generalization issues.
- Proposal: reuse pretrained video diffusion models as latent world simulators to surface their implicit 3D priors.
- Benefit: dense geometric supervision for MLLMs with no explicit 3D annotations required.