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20242026
most citedMetaUrban: An Embodied AI Simulation Platform for Urban Micromobility

2 citations · 2 across the 11 of their papers we have counts for

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cs.CV2026

Visually-grounded Humanoid Agents

Hang Ye, Xiaoxuan Ma, Fan Lu +3

Digital human generation has been studied for decades and supports a wide range of real-world applications. However, most existing systems are passively animated, relying on privil…

cs.CV2025

UrbanVerse: Scaling Urban Simulation by Watching City-Tour Videos

Mingxuan Liu, Honglin He, Elisa Ricci +2

Urban embodied AI agents, ranging from delivery robots to quadrupeds, are increasingly populating our cities, navigating chaotic streets to provide last-mile connectivity. Training…

cs.CV2025

From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning

Honglin He, Yukai Ma, Brad Squicciarini +2

Navigation foundation models trained on massive web-scale data enable agents to generalize across diverse environments and embodiments. However, these models, which are trained sol…

cs.CV2025

Towards Autonomous Micromobility through Scalable Urban Simulation

Wayne Wu, Honglin He, Chaoyuan Zhang +5

Micromobility, which utilizes lightweight mobile machines moving in urban public spaces, such as delivery robots and mobility scooters, emerges as a promising alternative to vehicu…

cs.CV2025

Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation

Ziyang Xie, Zhizheng Liu, Zhenghao Peng +2

Sim-to-real gap has long posed a significant challenge for robot learning in simulation, preventing the deployment of learned models in the real world. Previous work has primarily…

cs.CV2025

Joint Optimization for 4D Human-Scene Reconstruction in the Wild

Zhizheng Liu, Joe Lin, Wayne Wu +1

Reconstructing human motion and its surrounding environment is crucial for understanding human-scene interaction and predicting human movements in the scene. While much progress ha…

cs.CV2024

Learning to Generate Diverse Pedestrian Movements from Web Videos with Noisy Labels

Zhizheng Liu, Joe Lin, Wayne Wu +1

Understanding and modeling pedestrian movements in the real world is crucial for applications like motion forecasting and scene simulation. Many factors influence pedestrian moveme…

cs.CV2024★ 2 cited

MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility

Wayne Wu, Honglin He, Jack He +5

Public urban spaces like streetscapes and plazas serve residents and accommodate social life in all its vibrant variations. Recent advances in Robotics and Embodied AI make public…