From the 1 of 5 linked papers with an AI index.
5 papers
ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation
Yu Zhang, Yidi Shao, Wenqi Ouyang +5
ClothTransformer reformulates cloth simulation as an autoregressive sequence modeling problem in a learned latent space, using a unified Transformer architecture that handles diver…
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…