collaborators

8 papers

cs.CV2026

IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer

Zhengyu Zou, Hao Li, Kuixuan Jiao +7

Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spa…

cs.CV2026

S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence

Yalun Dai, Hao Li, Shulin Tian +10

Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inf…

cs.CV2026

PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning

Shaoxuan Li, Zhixuan Zhao, Hanze Deng +9

We introduce PerceptionComp, a manually annotated benchmark for complex, long-horizon, perception-centric video reasoning. PerceptionComp is designed so that no single moment is su…

cs.RO2026

From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors

Zhengshen Zhang, Hao Li, Yalun Dai +10

Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptabilit…

cs.CV2026

Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence

Yuanyuan Gao, Hao Li, Yifei Liu +14

The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding…

cs.RO2026

BFA++: Hierarchical Best-Feature-Aware Token Prune for Multi-View Vision Language Action Model

Haosheng Li, Weixin Mao, Zihan Lan +6

Vision-Language-Action (VLA) models have achieved significant breakthroughs by leveraging Large Vision Language Models (VLMs) to jointly interpret instructions and visual inputs. H…