3 citations · 3 across the 5 of their papers we have counts for
7 papers
GameDirector: Decoupling Gameplay Logic from Rendering for Player-Configurable Game World Models
Zijun Lin, Zhiyang Deng, Yuzhe Wu +2
Recent game world models support realistic visual simulation and interactive gameplay based on player inputs. However, they typically learn environment dynamics from pixel-level su…
StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation
Zijun Lin, Zeqing Wang, Cheston Tan +2
Recent game world models can generate visually realistic and interactive environments conditioned on player actions. However, games are not defined by pixels alone; they are govern…
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Zijun Lin, Jiafei Duan, Haoquan Fang +4
Recent advances in robotic manipulation have integrated low-level robotic control into Vision-Language Models (VLMs), extending them into Vision-Language-Action (VLA) models. Altho…
GroundFlow: A Plug-in Module for Temporal Reasoning on 3D Point Cloud Sequential Grounding
Zijun Lin, Shuting He, Cheston Tan +1
Sequential grounding in 3D point clouds (SG3D) refers to locating sequences of objects by following text instructions for a daily activity with detailed steps. Current 3D visual gr…
Human-like compositional learning of visually-grounded concepts using synthetic environments
Zijun Lin, M Ganesh Kumar, Cheston Tan
The compositional structure of language enables humans to decompose complex phrases and map them to novel visual concepts, showcasing flexible intelligence. While several algorithm…
FlowPlan: Zero-Shot Task Planning with LLM Flow Engineering for Robotic Instruction Following
Zijun Lin, Chao Tang, Hanjing Ye +1
Robotic instruction following tasks require seamless integration of visual perception, task planning, target localization, and motion execution. However, existing task planning met…
Compositional Learning of Visually-Grounded Concepts Using Reinforcement
Zijun Lin, Haidi Azaman, M Ganesh Kumar +1
Children can rapidly generalize compositionally-constructed rules to unseen test sets. On the other hand, deep reinforcement learning (RL) agents need to be trained over millions o…