6 papers
MIND: Multi-Scale Intent Diffusion for Text-Driven Physics-Based Humanoid Control
Bin Li, Ruichi Zhang, Han Liang +4
Enabling physics-based humanoids to execute diverse behaviors from high-level textual commands remains a significant challenge. Existing methods typically follow either a two-stage…
SCRIPT: Scalable Diffusion Policy with Multi-stage Training for Language-driven Physics-Based Humanoid Control
Jingyan Zhang, Han Liang, Ruichi Zhang +6
Controlling physics-based humanoids from natural-language instructions is a critical step toward general-purpose embodied agents. However, existing methods remain constrained by a…
InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs
Bin Li, Ruichi Zhang, Han Liang +6
Humanoid agents are expected to emulate the complex coordination inherent in human social behaviors. However, existing methods are largely confined to single-agent scenarios, overl…
Mojito: LLM-Aided Motion Instructor with Jitter-Reduced Inertial Tokens
Ziwei Shan, Yaoyu He, Chengfeng Zhao +5
Human bodily movements convey critical insights into action intentions and cognitive processes, yet existing multimodal systems primarily focused on understanding human motion via…
HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment
Juze Zhang, Jingyan Zhang, Zining Song +6
Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities. However, recent advances in modeling human-object interactio…
Capturing the Unseen: Vision-Free Facial Motion Capture Using Inertial Measurement Units
Youjia Wang, Yiwen Wu, Hengan Zhou +9
We present Capturing the Unseen (CAPUS), a novel facial motion capture (MoCap) technique that operates without visual signals. CAPUS leverages miniaturized Inertial Measurement Uni…