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cs.CV2026

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

Tianjie Ju, Zheng Wu, Yueqing Sun +15

Multimodal large language models (MLLMs) can interpret a street view, but reliable urban action depends on whether such local evidence remains useful after the agent starts to move…

cs.CV2026

HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning

Pengcheng Zhou, Xuanyu Liu, Yanchen Yin +4

Recent advances in Vision-Language Models (VLMs) have significantly improved image geo-localization, yet existing models remain susceptible to landmark bias, causing them to overlo…

cs.CV2026

Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking

Shengqiong Wu, Bobo Li, Xinkai Wang +6

Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework. However, existing approaches la…

cs.CV2026

Orthogonal Spatial-temporal Distributional Transfer for 4D Generation

Wei Liu, Shengqiong Wu, Bobo Li +4

In the AIGC era, generating high-quality 4D content has garnered increasing research attention. Unfortunately, current 4D synthesis research is severely constrained by the lack of…

cs.CV2026

Global Commander and Local Operative: A Dual-Agent Framework for Scene Navigation

Kaiming Jin, Yuefan Wu, Shengqiong Wu +3

Vision-and-Language Scene navigation is a fundamental capability for embodied human-AI collaboration, requiring agents to follow natural language instructions to execute coherent a…