9 papers
Infinite Worlds with Versatile Interactions
Zelin Gao, Qiuyu Wang, Jiapeng Zhu +17
We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded in…
Tac-DINO: Learning Vision-Tactile Features with Patch Alignment
Hong Li, Yankang Dong, Yue Xu +8
Touch is the primary medium through which humans interact with the environment. Currently, tactile learning mainly focuses on image-level pretraining or alignment. However, tactile…
GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation
Jiahao Yang, Zihan Wang, Xiangyang Li +4
Despite significant progress in Vision-Language Navigation (VLN), existing approaches still rely on dense RGB videos that produce excessive patch tokens and lack explicit spatial s…
CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives
Yihao Meng, Zichen Liu, Hao Ouyang +11
Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single scene; it requires progressing…
Advancing Open-source World Models
Robbyant Team, Zelin Gao, Qiuyu Wang +21
We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It…
PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models
Qiyuan Zhang, Biao Gong, Shuai Tan +7
Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation. This gap highlights a critical limitat…