{"type":"ScholarlyArticle","title":"Human-Centric Foundation Models: Perception, Generation and Agentic Modeling","authors":[{"name":"Shixiang Tang","slug":"shixiang-tang","given":"Shixiang","family":"Tang","affiliation":""},{"name":"Yizhou Wang","slug":"yizhou-wang","given":"Yizhou","family":"Wang","affiliation":""},{"name":"Lu Chen","slug":"lu-chen","given":"Lu","family":"Chen","affiliation":""},{"name":"Yuan Wang","slug":"yuan-wang","given":"Yuan","family":"Wang","affiliation":""},{"name":"Sida Peng","slug":"sida-peng","given":"Sida","family":"Peng","affiliation":""},{"name":"Dan Xu","slug":"dan-xu","given":"Dan","family":"Xu","affiliation":""},{"name":"Wanli Ouyang","slug":"wanli-ouyang","given":"Wanli","family":"Ouyang","affiliation":""}],"abstract":"Human understanding and generation are critical for modeling digital humans and humanoid embodiments. Recently, Human-centric Foundation Models (HcFMs)—inspired by the success of generalist models such as large language and vision models—have emerged to unify diverse human-centric tasks into a single framework, surpassing traditional task-specific approaches. In this survey, we present a comprehensive overview of HcFMs by proposing a taxonomy that categorizes current approaches into four groups: (1) Human-centric Perception Foundation Models that capture fine-grained features for multi-modal 2D and 3D understanding; (2) Human-centric AIGC Foundation Models that generate high-fidelity, diverse human-related content; (3) Unified Perception and Generation Models that integrate these capabilities to enhance both human understanding and synthesis; and (4) Human-centric Agentic Foundation Models that extend beyond perception and generation to learn human-like intelligence and interactive behaviors for humanoid embodied tasks. We review state-of-the-art techniques, discuss emerging challenges and future research directions. This survey aims to serve as a roadmap for researchers and practitioners working towards more robust, versatile, and intelligent digital human and embodiments modeling. Website is https://github.com/HumanCentricModels/Awesome-Human-Centric-Foundation-Models/","doi":"10.24963/ijcai.2025/1185","url":"https://ijcai.org/proceedings/2025/1185","pdf":"https://ijcai.org/proceedings/2025/1185.pdf","markdown":"https://ijcai.org/proceedings/2025/1185.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1185","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Survey Track","track":"Survey Track","number":1185,"pages":{"first":10678,"last":10686},"keywords":["Computer Vision: CV: Embodied vision: Active agents, simulation","Computer Vision: CV: Biometrics, face, gesture and pose recognition","Computer Vision: CV: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}