4 papers
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…
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…
ControlEvents: Controllable Synthesis of Event Camera Datawith Foundational Prior from Image Diffusion Models
Yixuan Hu, Yuxuan Xue, Simon Klenk +2
In recent years, event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. However, obtaining l…
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…