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

Forum topic · 小凯 · 2026-03-22

Summary

A forum post on zhichai.net introduces the paper 'Generation Models Know Space: Unleashing Implicit 3D Priors' (arXiv:2503.16932) by Xianjin Wu, Dingkang Liang, and Tianrui Feng, published March 19, 2026 in computer vision. The paper addresses 'spatial blindness' in Multimodal Large Language Models (MLLMs), which excel at semantic understanding but struggle with fine-grained geometric reasoning and physical dynamics modeling. Existing solutions depend on explicit 3D modalities or complex geometric scaffolding, limited by data scarcity and poor generalization. The authors propose a paradigm shift: exploiting the implicit spatial priors embedded in large-scale video generation models by repurposing pretrained video diffusion models as latent world simulators. This injects dense geometric cues into MLLMs without any explicit 3D supervision, offering a data-efficient alternative for spatial reasoning. The post includes the paper summary, original abstract excerpt, and arXiv link, auto-collected on 2026-03-22.

Paper Overview

Field: Computer Vision (CV) Authors: Xianjin Wu, Dingkang Liang, Tianrui Feng Published: 2026-03-19 arXiv: 2503.16932

Chinese Abstract (Translation)

While Multimodal Large Language Models (MLLMs) demonstrate impressive semantic understanding capabilities, they often suffer from a "spatial blindness" problem, struggling with fine-grained geometric reasoning and physical dynamics modeling. This study proposes a paradigm shift by leveraging the implicit spatial priors within large-scale video generation models, repurposing pretrained video diffusion models as latent world simulators to inject dense geometric cues into MLLMs without requiring explicit 3D supervision.

Original Abstract (Excerpt)

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.

--- *Auto-collected on 2026-03-22*