7 papers
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…
OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation
Youquan Liu, Weidong Yang, Ao Liang +9
LiDAR scene generation is increasingly important for scalable simulation and synthetic data creation, especially under diverse sensing conditions that are costly to capture at scal…
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…
3D and 4D World Modeling: A Survey
Lingdong Kong, Wesley Yang, Yu Yang +21
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphas…
SPIRAL: Semantic-Aware Progressive LiDAR Scene Generation and Understanding
Dekai Zhu, Yixuan Hu, Youquan Liu +3
Leveraging recent diffusion models, LiDAR-based large-scale 3D scene generation has achieved great success. While recent voxel-based approaches can generate both geometric structur…
Generative Data Augmentation for Object Point Cloud Segmentation
Dekai Zhu, Stefan Gavranovic, Flavien Boussuge +2
Data augmentation is widely used to train deep learning models to address data scarcity. However, traditional data augmentation (TDA) typically relies on simple geometric transform…