Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for plann...
Yu-Han Guo, Jin-Ming Liu, Liang Xu et al.· 0 citations
Humanoid robots are increasingly expected to serve as embodied social agents that communicate naturally with humans through face-to-face interaction. During such communication, humanoid robots require body behaviors that are synchronized with speech, affectively expressive, and suitable for real-time execution. However...
Cheng-Qun Yang, Teng-Jie Zhu, Liang Xu et al.· 0 citations
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for plann...
Yu-Han Guo, Jin-Ming Liu, Liang Xu et al.· 0 citations
To serve as real-world personal assistants, streaming video models need persistent memory that retains past experiences for later use. Yet existing streaming benchmarks and methods often focus on individual continuous videos or short clips, overlooking that real-world interactions are often intermittent and require mem...
Jian-Guo Huang, Jin-Ming Liu, Qi-Yao Wang et al.· 0 citations
This work presents Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis, and proposes OpenHHI, a single and unified HHI representation and modeling framework that jointly optimizes interaction reconstruction and semantic understanding.
Liang Xu, Cheng-Qun Yang, Zili Lin et al.· 0 citations
A unified spatiotemporally decoupled framework named DeMoDiff is proposed, which jointly redesigns representation and architecture and incorporates spatial-temporal masking and attention mechanisms into an autoregressive diffusion generator, achieving both generative capability and controllable editability.
Cheng-Qun Yang, Liang Xu, Yan-Ping Li et al.· 0 citations
This work proposes a lightweight granularity-aware model anchored at a frozen standard-caption-aligned retrieval model that improves mixed-granularity retrieval without compromising standard-caption performance, and believes that its MRBench provides a comprehensive testbed for advancing motion-language alignment evalu...
Fulong Liu, Liang Xu, Chengqun Yang et al.· 0 citations
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