7 papers · 1 filter
InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation
Zhuoran Yang, Xi Guo, Chenjing Ding +3
Autonomous driving relies on robust models trained on high-quality, large-scale multi-view driving videos. While world models offer a cost-effective solution for generating realist…
AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs
Boyu Chang, Qi Wang, Xi Guo +3
Visual abductive reasoning (VAR) is a challenging task that requires AI systems to infer the most likely explanation for incomplete visual observations. While recent MLLMs develop…
Physical Informed Driving World Model
Zhuoran Yang, Xi Guo, Chenjing Ding +2
Autonomous driving requires robust perception models trained on high-quality, large-scale multi-view driving videos for tasks like 3D object detection, segmentation and trajectory…
InfinityDrive: Breaking Time Limits in Driving World Models
Xi Guo, Chenjing Ding, Haoxuan Dou +3
Autonomous driving systems struggle with complex scenarios due to limited access to diverse, extensive, and out-of-distribution driving data which are critical for safe navigation.…
DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generation
Wei Wu, Xi Guo, Weixuan Tang +4
Recent advancements in generative models have provided promising solutions for synthesizing realistic driving videos, which are crucial for training autonomous driving perception m…
MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control
Yining Yao, Xi Guo, Chenjing Ding +1
High-quality driving video generation is crucial for providing training data for autonomous driving models. However, current generative models rarely focus on enhancing camera moti…