---
title: "Human-Centric Foundation Models: Perception, Generation and Agentic Modeling"
authors:
  - "Shixiang Tang"
  - "Yizhou Wang"
  - "Lu Chen"
  - "Yuan Wang"
  - "Sida Peng"
  - "Dan Xu"
  - "Wanli Ouyang"
doi: "10.24963/ijcai.2025/1185"
url: "https://ijcai.org/proceedings/2025/1185"
pdf: "https://ijcai.org/proceedings/2025/1185.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10678-10686"
track: "Survey Track"
keywords:
  - "Computer Vision: CV: Embodied vision: Active agents, simulation"
  - "Computer Vision: CV: Biometrics, face, gesture and pose recognition"
  - "Computer Vision: CV: Representation learning"
---

# Human-Centric Foundation Models: Perception, Generation and Agentic Modeling

Shixiang Tang, Yizhou Wang, Lu Chen, Yuan Wang, Sida Peng, Dan Xu, Wanli Ouyang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10678-10686. DOI: [10.24963/ijcai.2025/1185](https://doi.org/10.24963/ijcai.2025/1185)

## 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/