Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding

While Multimodal Large Language Models demonstrate im-pressive 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 complexgeometric scaffolding, which are limited by data scarcity and generaliza-tion challenges. In this work, we propose a paradigm shift by leveragingthe implicit spatial prior within large-scale video generation models. Weposit that to synthesize temporally coherent videos, these models inher-ently learn robust 3D structural priors and physical laws. We introduceVEGA-3D (Video Extracted Generative Awareness), a plug-and-playframework that repurposes a pre-trained video diffusion model as a La-tent World Simulator. By extracting spatiotemporal features from inter-mediate noise levels and integrating them with semantic representationsvia a token-level adaptive gated fusion mechanism, we enrich MLLMswith dense geometric cues without explicit 3D supervision. Extensiveexperiments across 3D scene understanding, spatial reasoning, and em-bodied manipulation benchmarks demonstrate that our method outper-forms state-of-the-art baselines, validating that generative priors providea scalable foundation for physical-world understanding. Code is publiclyavailable at https://github.com/H-EmbodVis/VEGA-3D.