Poster

Scaling Spatial Intelligence with Multimodal Foundation Models

Zhongang Cai ⋅ Wang Ruisi ⋅ Chenyang Gu ⋅ Fanyi Pu ⋅ Junxiang Xu ⋅ YUBO WANG ⋅ Wanqi Yin ⋅ Zhitao Yang ⋅ Chen Wei ⋅ Tongxi Zhou ⋅ Qingping SUN ⋅ Hui En Pang ⋅ Jiaqi Li ⋅ Oscar Qian ⋅ Zhiqian Lin ⋅ Xuanke Shi ⋅ Kewang Deng ⋅ Xiaoyang Han ⋅ Zukai Chen ⋅ Xiangyu Fan ⋅ Hanming Deng ⋅ Lewei Lu ⋅ Liang Pan ⋅ Bo Li ⋅ Ziwei Liu ⋅ Quan Wang ⋅ Dahua Lin ⋅ Lei Yang

Project Page

Abstract

Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SSI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SSI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SSI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks: 68.7% on VSI-Bench, 43.3% on MMSI, 85.6% on MindCube, and 54.6% on ViewSpatial, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En). More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. All newly trained multimodal foundation models are publicly released to facilitate further research in this direction.