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arXiv · 2608.14138

SPARGen: Unifying Spatial Perception and Reasoning through Native Multimodal Generation

Jinsheng Quan·Jianhua Li·Siyi Xie·Xuanke Shi·Kewang Deng·Zukai Chen·Feifei Shao·Lei Yang·Quan Wang·Yawei Luo
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Abstract

Spatial perception and reasoning from visual observations require recovering geometric structure, establishing correspondences, and understanding spatial relations. Existing approaches typically address these capabilities separately using task-specific architectures or external geometric modules, limiting knowledge transfer among complementary representations of the same physical scene. We introduce SPARGen, a unified multimodal framework that casts 3D reconstruction, dense correspondence, and spatial reasoning as instruction-conditioned generation tasks. SPARGen serializes compact structured and linguistic outputs as token sequences while generating dense geometric fields in image-aligned forms, enabling spatial supervision to jointly shape shared representations within a native multimodal generative model. Experiments across benchmarks for 3D reconstruction, correspondence, and spatial reasoning show that SPARGen achieves competitive performance across heterogeneous spatial tasks within a single native multimodal generative framework.

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Jinsheng Quan, Jianhua Li, Siyi Xie, Xuanke Shi, Kewang Deng, Zukai Chen, Feifei Shao, Lei Yang, Quan Wang, Yawei Luo. 2026-08-14. SPARGen: Unifying Spatial Perception and Reasoning through Native Multimodal Generation. https://arxiv.org/abs/2608.14138

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