activity
20242026
collaborators

7 papers

cs.CV2026

SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios

Juntao Cheng, Wanyue Zhang, Zhiwei Yu +7

Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday human-built environments. Interacting w…

cs.CV2026

World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning

Wanyue Zhang, Wenxiang Wu, Wang Xu +6

Vision-language models (VLMs) have shown strong performance on static visual understanding, yet they still struggle with dynamic spatial reasoning that requires imagining how scene…

cs.CV2026

Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning

Yibin Huang, Wang Xu, Wanyue Zhang +6

Spatial intelligence is a critical frontier for Multimodal Large Language Models (MLLMs), empowering them to comprehend the physical world. Drawing inspiration from human perceptio…

cs.CV2025

MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?

Shiqi Dai, Zizhi Ma, Zhicong Luo +8

While Multimodal Large Language Models (MLLMs) have exhibited remarkable general intelligence across diverse domains, their potential in low-altitude applications dominated by Unma…

cs.AI2025

Scaling Spatial Reasoning in MLLMs through Programmatic Data Synthesis

Zhi Helu, Huang Jingjing, Xu Wang +9

Embodied intelligence, a grand challenge in artificial intelligence, is fundamentally constrained by the limited spatial understanding and reasoning capabilities of current models.…

cs.CV2025

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture

Wanyue Zhang, Yibin Huang, Yangbin Xu +5

Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, ex…