4 papers
Native Video-Action Pretraining for Generalizable Robot Control
Qihang Zhang, Lin Li, Luyao Zhang +26
The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation i…
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…
SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook
Chenjing Ding, Chiyu Wang, Boshi Liu +3
Vector quantization (VQ) is a method for deterministically learning features through discrete codebook representations. Recent works have utilized visual tokenizers to discretize v…