基礎模型時代中以人為中心的智能：綜述

摘要

在以基礎模型為代表的時代，以人為中心的智慧正持續演進，日益強調規模化、可遷移性與通用建模能力。然而，該領域尚未與基礎模型充分融合，以達到與之相當的進展。更為重要的是，這一廣闊領域內的最新進展仍因任務、模態及研究社群的不同而呈現碎片化狀態，使得其內在的概念與方法論關聯尚不明確。為彌合這些鴻溝並重新審視基礎模型時代以人為中心的智慧，我們提出了一套全光譜的人類情境分類體系，透過六個相互關聯的層級加以整合：將人類視為可觀察的主體（經由視覺外觀與空間幾何）、視為動態的行為者（經由運動動力學與互動建模），以及視為情境化的智能體（經由世界模擬與具身能動性）。接著，我們呈現該領域的方法論基礎，涵蓋以人為中心的資料家族、計算架構範式，以及具代表性的訓練與推論最佳化策略。隨後，我們系統性地回顧了這些層級中具代表性的方法，並整理了相關的資料集、基準測試與評估指標。我們進一步探討了朝向可擴展、可信賴、具物理基礎且可部署的以人為中心智慧所面臨的開放性挑戰與前景可期的研究方向，旨在為推進該領域提供一個連貫的框架與實用參考。最後，我們在專案頁面上提供了一個系統化組織且持續更新的以人為中心人工智慧文獻與資源集合。

English

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.