发表机构
The Hong Kong Polytechnic University; Peking University; University of Southern Queensland; Tongji University; RIKEN Center for Advanced Intelligence Project; Beijing Institute of Technology; The University of Sydney; University of Trento; Alibaba Group; Nanyang Technological University; Hong Kong University of Science and Technology（香港理工大学; 北京大学; 南昆士兰大学; 同济大学; 理化学研究所先进智能项目中心; 北京理工大学; 悉尼大学; 特伦托大学; 阿里巴巴集团; 南洋理工大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对基础模型时代以人为中心的智能进展碎片化问题，提出全谱人类上下文分类法，梳理其方法基础、相关方法与资源，探讨挑战方向，为该领域推进提供参考。
AI 中文摘要
基础模型时代下，以人为中心的智能正不断发展，人们愈发关注其规模、可迁移性及通用建模能力，但该领域尚未与基础模型充分融合，以达到基础模型所取得的同等进展。更重要的是，近期该广阔领域的进展在任务、模态和研究社区间仍呈碎片化状态，导致其内在概念与方法关联尚不明确。为弥合这些分歧并重新思考基础模型时代的以人为中心的智能，本文引入了全谱人类上下文分类法，该分类法将人类视为可通过视觉外观和空间几何观测的主体、通过运动动力学与交互建模的动态行动者、通过世界模拟与具身能动性的情境化主体，整合为六个相互关联的层级。接下来，本文阐述了该领域的方法学基础，涵盖以人为中心的数据族、计算架构范式以及代表性的训练与推理优化策略。随后，本文系统综述了各层级的代表性方法，并整理了相关的数据集、基准及评估指标。此外，本文还探讨了可扩展、可信、物理接地且可部署的以人为中心的智能所面临的开放挑战与有前景的研究方向，旨在为该领域的推进提供连贯框架与实用参考。最后，本文在项目页面上提供了系统整理且持续更新的以人为中心的AI文献与资源集合。
英文摘要
Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated with foundation models to achieve the comparable progress seen in them. More importantly, recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear. To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonomy that integrates six interconnected levels by viewing humans as observable subjects through visual appearance and spatial geometry, as dynamic actors through kinematic dynamics and interaction modeling, and as situated agents through world simulation and embodied agency. We next present the methodological foundations of the field, covering human-centric data families, computational architecture paradigms, and representative training and inference optimization strategies. We then systematically review representative methods across these levels and organize the associated datasets, benchmarks, and evaluation metrics. We further discuss open challenges and promising research directions toward human-centric intelligence that is scalable, trustworthy, physically grounded, and deployable, aiming to provide a coherent framework and practical reference for advancing the field. Finally, we provide a systematically organized and continuously updated collection of human-centric AI literature and resources on our project page.
CommentsGitHub Repo: https://github.com/cseeyangchen/Human-Centric-AI; Project Page: https://cseeyangchen.github.io/Human-Centric-AI/homepage/
