7 papers · 1 filter
PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
Yu Zhang, Xudong Xu, Xingang Pan
Neural simulation of high-fidelity deformable bodies is a foundational challenge in computer graphics and physical AI. Long-horizon prediction for high-resolution 3D volumetric mes…
RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts
Hongbin Lin, Chaoda Zheng, Yiming Yang +11
End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their own actions, leading to caus…
VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis
Kangjie Chen, Xiangyu Li, Dongbin Zhang +12
We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods face a fundamental trade-off: r…
X-Cache: Cross-Chunk Block Caching for Few-Step Autoregressive World Models Inference
Yixiao Zeng, Jianlei Zheng, Chaoda Zheng +10
Real-time world simulation is becoming a key infrastructure for scalable evaluation and online reinforcement learning of autonomous driving systems. Recent driving world models bui…
X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving
Chaoda Zheng, Sean Li, Jinhao Deng +9
Scalable and reliable evaluation is increasingly critical in the end-to-end era of autonomous driving, where vision--language--action (VLA) policies directly map raw sensor streams…
FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model
Hongbin Lin, Yiming Yang, Yifan Zhang +10
In autonomous driving, end-to-end planners learn scene representations from raw sensor data and utilize them to generate a motion plan or control actions. However, exclusive relian…
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
Qiang Li, Yingwenqi Jiang, Tuoxi Li +17
Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large vi…