6 citations · 6 across the 8 of their papers we have counts for
10 papers
UNIVERSE: Unified Video Action Models for Autonomous Driving with Flexible Mask-Modulated Modality Generation
Mengmeng Liu, Diankun Zhang, Jiuming Liu +7
World Action Models (WAMs) have shown strong potential for improving action generalization in autonomous driving by using future video prediction as dense supervision for scene dyn…
Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends
Jiuming Liu, Chaojun Ni, Mengmeng Liu +7
With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, benefiting various downstream do…
DriveVA: Video Action Models are Zero-Shot Drivers
Mengmeng Liu, Diankun Zhang, Jiuming Liu +7
Generalization is a central challenge in autonomous driving, as real-world deployment requires robust performance under unseen scenarios, sensor domains, and environmental conditio…
RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer
Jiuming Liu, Guangming Wang, Zhe Liu +7
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale LiDAR registration methods has been rarely explored before. Challe…
4DSTR: Advancing Generative 4D Gaussians with Spatial-Temporal Rectification for High-Quality and Consistent 4D Generation
Mengmeng Liu, Jiuming Liu, Yunpeng Zhang +4
Remarkable advances in recent 2D image and 3D shape generation have induced a significant focus on dynamic 4D content generation. However, previous 4D generation methods commonly s…
DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion
Mengmeng Liu, Michael Ying Yang, Jiuming Liu +5
Visual-LiDAR odometry is a critical component for autonomous system localization, yet achieving high accuracy and strong robustness remains a challenge. Traditional approaches comm…
Tracing the Influence of Predecessors on Trajectory Prediction
Mengmeng Liu, Hao Cheng, Michael Ying Yang
In real-world traffic scenarios, agents such as pedestrians and car drivers often observe neighboring agents who exhibit similar behavior as examples and then mimic their actions t…
An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images
Hao Cheng, Mengmeng Liu, Lin Chen
Perception that involves multi-object detection and tracking, and trajectory prediction are two major tasks of autonomous driving. However, they are currently mostly studied separa…
LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints
Mengmeng Liu, Hao Cheng, Lin Chen +5
Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints. Existing methods typically rely on…
GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model
Hao Cheng, Mengmeng Liu, Lin Chen +3
Trajectory prediction has been a long-standing problem in intelligent systems like autonomous driving and robot navigation. Models trained on large-scale benchmarks have made signi…