10 papers
GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory
Hu Zhu, Bohan Li, Xianda Guo +6
Semantic occupancy provides a structured spatial memory for embodied indoor agents by jointly representing occupied regions, observed free space, unknown areas, and object semantic…
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
Ao Liang, Lingdong Kong, Tianyi Yan +19
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…
Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method
Bohan Li, Xin Jin, Hu Zhu +9
Driving scene generation is a critical domain for autonomous driving, enabling downstream applications, including perception and planning evaluation. Occupancy-centric methods have…
Is Your Driving World Model an All-Around Player?
Lingdong Kong, Ao Liang, Tianyi Yan +20
Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic phys…
ReWorld: Multi-Dimensional Reward Modeling for Embodied World Models
Baorui Peng, Wenyao Zhang, Liang Xu +5
Recently, video-based world models that learn to simulate the dynamics have gained increasing attention in robot learning. However, current approaches primarily emphasize visual ge…
MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving
Hongsi Liu, Jun Liu, Guangfeng Jiang +1
As one of the automotive sensors that have emerged in recent years, 4D millimeter-wave radar has a higher resolution than conventional 3D radar and provides precise elevation measu…