3 citations · 6 across the 21 of their papers we have counts for
27 papers
Evo-DexBench: Benchmarking Generalization across Related Tasks for Dexterous Manipulation
Bing Cheng, Yiran Mao, Zewei Ye +10
General-purpose dexterous manipulation requires policies to transfer knowledge from related tasks to new tasks with only a few target demonstrations. Existing benchmarks often prov…
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance
Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang +10
Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation and various perception tasks…
Point as Skeleton: Accumulated Point Cloud Enhanced Autoregressive Generation for Closed-Loop Autonomous Driving Simulation
Songbur Wong, Xiaosong Jia, Junqi You +12
Evaluating end-to-end autonomous driving (E2E-AD) remains challenging, as existing driving simulation methods often trade off closed-loop interactivity (e.g., CARLA) and real-world…
LA4VLA: Learning to Act without Seeing via Language-Action Pretraining
Tao Lin, Yuxin Du, Yiran Mao +13
Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visu…
ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving
Zhiyuan Zhang, Yanlun Peng, Jianing Zhang +7
Reactive capability is a key property of data-driven behavior world model simulators for autonomous driving simulation systems. With this capability, simulated world agents can res…
GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization
Xiaosong Jia, Bowen Yang, Zuhao Ge +17
Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…
Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling
Yihang Wu, Yihang Sun, Shaofeng Zhang +4
Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e.g., RGB) and spatial inform…
Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations
Zhiyuan Zhang, Zhenghao Jin, Yanlun Peng +8
Robustness is a critical requirement for deploying autonomous driving systems in the real world. Existing robustness benchmarks for autonomous driving have made important progress…
Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
Tao Lin, Yuxin Du, Jiting Liu +14
Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often s…
Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models
Xiaosong Jia, Yuqian Shao, Zhenjie Yang +3
With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop eva…
Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving
Yuqian Shao, Xiaosong Jia, Langechuan Liu +1
End-to-end autonomous driving (E2E-AD) has achieved remarkable progress. However, one practical and useful function has been long overlooked: users may wish to customize the desire…
Efficient-LVSM: Faster, Cheaper, and Better Large View Synthesis Model via Decoupled Co-Refinement Attention
Xiaosong Jia, Yihang Sun, Junqi You +5
Feedforward models for novel view synthesis (NVS) have recently advanced by transformer-based methods like LVSM, using attention among all input and target views. In this work, we…
Spatial Retrieval Augmented Autonomous Driving
Xiaosong Jia, Chenhe Zhang, Yule Jiang +8
Existing autonomous driving systems rely on onboard sensors (cameras, LiDAR, IMU, etc) for environmental perception. However, this paradigm is limited by the drive-time perception…
DriveVGGT: Calibration-Constrained Visual Geometry Transformers for Multi-Camera Autonomous Driving
Xiaosong Jia, Yanhao Liu, Yu Hong +5
Feed-forward reconstruction has been progressed rapidly, with the Visual Geometry Grounded Transformer (VGGT) being a notable baseline. However, directly applying VGGT to autonomou…
LaGen: Towards Autoregressive LiDAR Scene Generation
Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang +7
Generative world models for autonomous driving (AD) are of great value in applications such as data augmentation, closed-loop simulation, and safety-critical scenario evaluation. U…
TrajTok: Technical Report for 2025 Waymo Open Sim Agents Challenge
Zhiyuan Zhang, Xiaosong Jia, Guanyu Chen +2
In this technical report, we introduce TrajTok, a trajectory tokenizer for discrete next-token-prediction based behavior generation models, which combines data-driven and rule-base…
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
Zhenjie Yang, Xiaosong Jia, Qifeng Li +3
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E…
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
Zhenjie Yang, Yilin Chai, Xiaosong Jia +5
End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensory data and robust handling of diverse and complex driving scenarios, particularly rare maneu…
DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
Xiaosong Jia, Junqi You, Zhiyuan Zhang +1
End-to-end autonomous driving (E2E-AD) has emerged as a trend in the field of autonomous driving, promising a data-driven, scalable approach to system design. However, existing E2E…
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model
Junqi You, Xiaosong Jia, Zhiyuan Zhang +2
For end-to-end autonomous driving (E2E-AD), the evaluation system remains an open problem. Existing closed-loop evaluation protocols usually rely on simulators like CARLA being les…
Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation
Shaobo Wang, Yantai Yang, Qilong Wang +3
Dataset Distillation (DD) aims to synthesize a small dataset capable of performing comparably to the original dataset. Despite the success of numerous DD methods, theoretical explo…
FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Driving
Yutao Zhu, Xiaosong Jia, Xinyu Yang +1
The integration of data from diverse sensor modalities (e.g., camera and LiDAR) constitutes a prevalent methodology within the ambit of autonomous driving scenarios. Recent advance…
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
Xiaosong Jia, Zhenjie Yang, Qifeng Li +2
In an era marked by the rapid scaling of foundation models, autonomous driving technologies are approaching a transformative threshold where end-to-end autonomous driving (E2E-AD)…
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
Xiaosong Jia, Shaoshuai Shi, Zijun Chen +4
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the pos…