Human-Centric Intelligence in the Era of Foundation Models: A Survey
- URL: http://arxiv.org/abs/2608.18184v1
- Date: Tue, 18 Aug 2026 08:12:40 GMT
- Title: Human-Centric Intelligence in the Era of Foundation Models: A Survey
- Abstract summary: Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling.<n>Recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear.<n>To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonomy.
- Score: 159.29291047252138
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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. 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.
Related papers
- Monocular Depth Estimation from a Single Image: Progress and Opportunities [51.389814411876294]
This survey traces the field's evolution from early learning-based methods to the emergence of transformative foundation models.<n>We highlight the integration of depth estimation into applications such as visual SLAM, content generation, and robot perception.
arXiv Detail & Related papers (2026-09-01T12:48:10Z) - Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer [55.982669669569624]
This survey presents the first systematic review of foundation-model priors for hand-object interaction.<n>We establish a taxonomy of eight foundation-model sub-priors grouped into geometric, semantic, and visual families.<n>We examine how HOI-derived knowledge is used in robot learning, including human-data pretraining, human-to-robot skill transfer, and HOI-to-robot data generation.
arXiv Detail & Related papers (2026-07-30T15:48:16Z) - World Action Models: The Next Frontier in Embodied AI [123.5787299299832]
Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning.<n>They learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention.<n>A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline.
arXiv Detail & Related papers (2026-05-12T13:10:52Z) - Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap [10.048113624715151]
Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence.<n>This paper provides a comprehensive and structured survey of the field, from its formal task definition to the current state of the art.
arXiv Detail & Related papers (2026-04-15T09:20:02Z) - Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems [75.78934957242403]
Self-driving vehicles and drones require true Spatial Intelligence from multi-modal onboard sensor data.<n>This paper presents a framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal.
arXiv Detail & Related papers (2025-12-30T17:58:01Z) - FreeAskWorld: An Interactive and Closed-Loop Simulator for Human-Centric Embodied AI [24.545163508739943]
FreeAskWorld is an interactive simulation framework that integrates large language models for high-level behavior planning and semantically grounded interaction.<n>Our framework supports scalable, realistic human-agent simulations and includes a modular data generation pipeline tailored for diverse embodied tasks.<n>We present and publicly release FreeAskWorld, a large-scale benchmark dataset comprising reconstructed environments, six diverse task types, 16 core object categories, 63,429 annotated sample frames, and more than 17 hours of interaction data.
arXiv Detail & Related papers (2025-11-17T15:58:46Z) - Human-Centric Foundation Models: Perception, Generation and Agentic Modeling [79.97999901785772]
Human-centric Foundation Models unify diverse human-centric tasks into a single framework.<n>We present a comprehensive overview of HcFMs by proposing a taxonomy that categorizes current approaches into four groups.<n>This survey aims to serve as a roadmap for researchers and practitioners working towards more robust, versatile, and intelligent digital human and embodiments modeling.
arXiv Detail & Related papers (2025-02-12T16:38:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.