Human-Centric Intelligence in the Era of Foundation Models: A Survey

基础模型时代下的以人为本智能：综述

Abstract: 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.

摘要： 在基础模型时代，以人为本的智能（Human-centric intelligence）正在不断演进，并日益强调规模化、可迁移性和通用建模能力。然而，它尚未与基础模型实现充分融合，从而取得与之相当的进展。更重要的是，该领域近期在广泛的研究图景中取得的进展，在任务、模态和研究社区之间仍然处于碎片化状态，导致其内在的概念和方法论联系尚不明确。

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.

为了弥合这些鸿沟并重新思考基础模型时代的以人为本智能，我们引入了一种全谱系的人类情境分类法（full-spectrum human context taxonomy）。该分类法整合了六个相互关联的层级：通过视觉外观和空间几何将人类视为“可观测主体”；通过运动动力学和交互建模将人类视为“动态参与者”；以及通过世界模拟和具身智能将人类视为“情境化代理”。

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.

接下来，我们介绍了该领域的方法论基础，涵盖了以人为本的数据集族、计算架构范式，以及具有代表性的训练和推理优化策略。随后，我们系统地回顾了各层级中的代表性方法，并整理了相关的数据库、基准测试和评估指标。

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.

我们进一步探讨了迈向可扩展、可信、物理落地且可部署的以人为本智能所面临的开放性挑战及有前景的研究方向，旨在为推动该领域的发展提供一个连贯的框架和实践参考。最后，我们在项目页面上提供了一个系统整理且持续更新的以人为本人工智能文献与资源库。