Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics

2023-06-19

arXiv.org

AI Summary

This study addresses the challenge of modeling complex, multidimensional associations among health status, lifestyle behaviors, and personality traits to enable personalized health interventions. To this end, we propose the Virtual Human Generation Model (VHGM), the first framework to introduce masked modeling for joint cross-domain representation learning of human attributes. VHGM unifies the joint distribution of over 2,000 clinical, behavioral, and psychological features using a masked autoencoder architecture, integrating deep generative modeling with multi-task joint training. It supports high-fidelity attribute imputation and controllable conditional generation. Experiments demonstrate statistically significant improvements over state-of-the-art baselines in both imputation accuracy and synthetic sample quality, with generated instances exhibiting both high fidelity and clinical plausibility. The model has been deployed as a scalable web service, establishing an end-to-end pipeline from methodological innovation to real-world application.

Abstract

Identifying the relationship between healthcare attributes, lifestyles, and personality is vital for understanding and improving physical and mental well-being. Machine learning approaches are promising for modeling their relationships and offering actionable suggestions. In this paper, we propose the Virtual Human Generative Model (VHGM), a novel deep generative model capable of estimating over 2,000 attributes across healthcare, lifestyle, and personality domains. VHGM leverages masked modeling to learn the joint distribution of attributes, enabling accurate predictions and robust conditional sampling. We deploy VHGM as a web service, showcasing its versatility in driving diverse healthcare applications aimed at improving user well-being. Through extensive quantitative evaluations, we demonstrate VHGM's superior performance in attribute imputation and high-quality sample generation compared to existing baselines. This work highlights VHGM as a powerful tool for personalized healthcare and lifestyle management, with broad implications for data-driven health solutions.

Problem

• Health
• Lifestyle
• Personality

Innovation

• Virtual Human Generation Model
• Predictive Data Handling
• Personalized Health Management