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

• URL: http://arxiv.org/abs/2306.10656v4
• Date: Thu, 30 Jan 2025 01:49:06 GMT
• Title: Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
• Authors: Kenta Oono, Nontawat Charoenphakdee, Kotatsu Bito, Zhengyan Gao, Hideyoshi Igata, Masashi Yoshikawa, Yoshiaki Ota, Hiroki Okui, Kei Akita, Shoichiro Yamaguchi, Yohei Sugawara, Shin-ichi Maeda, Kunihiko Miyoshi, Yuki Saito, Koki Tsuda, Hiroshi Maruyama, Kohei Hayashi,
• Abstract summary: We propose a novel deep generative model capable of estimating over 2,000 attributes across healthcare, lifestyle, and personality domains. We deploy VHGM as a web service, showcasing its versatility in driving diverse healthcare applications.
• Score: 15.027129674236535
• License:
• 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.

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