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

Source: https://huggingface.co/papers/2608.18184 Authors:

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Abstract

This survey proposes a unified taxonomy and methodological framework for human-centric intelligence across visual, dynamic, and embodied levels within the foundation-model era.

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 withfoundation modelsto 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 throughkinematic dynamicsandinteraction modeling, and as situated agents throughworld simulationandembodied agency. We next present the methodological foundations of the field, covering human-centric data families,computational architecture paradigms, and representative training andinference optimizationstrategies. 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.

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