12 papers
DINO-Tok: Adapting DINO for Visual Tokenizers
Mingkai Jia, Mingxiao Li, Zhijian Shu +12
Recent advances in visual generation have emphasized the importance of Latent Generative Models (LGMs), which critically depend on effective visual tokenizers to bridge pixels and…
OccTENS: 3D Occupancy World Model via Temporal Next-Scale Prediction
Bu Jin, Songen Gu, Xiaotao Hu +5
In this paper, we propose OccTENS, a generative occupancy world model that enables controllable, high-fidelity long-term occupancy generation while maintaining computational effici…
Entropy-Guided k-Guard Sampling for Long-Horizon Autoregressive Video Generation
Yizhao Han, Tianxing Shi, Zhao Wang +6
Autoregressive (AR) architectures have achieved significant successes in LLMs, inspiring explorations for video generation. In LLMs, top-p/top-k sampling strategies work exceptiona…
LiteVGGT: Boosting Vanilla VGGT via Geometry-aware Cached Token Merging
Zhijian Shu, Cheng Lin, Tao Xie +8
3D vision foundation models like Visual Geometry Grounded Transformer (VGGT) have advanced greatly in geometric perception. However, it is time-consuming and memory-intensive for l…
ComDrive: Comfort-Oriented End-to-End Autonomous Driving
Junming Wang, Xingyu Zhang, Zebin Xing +7
We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrate…
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
Zebin Xing, Xingyu Zhang, Yang Hu +5
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable…