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This study investigates aesthetic preferences and visual attention patterns toward Chinese-style digital humans in the context of AI-generated content (AIGC). Combining a questionnaire (N = 136), semi-structured interviews, and eye-tracking experiments, the research identifies key design elements preferred by young consumers. Results show a strong preference for young female digital humans with extroverted personality traits, rendered in 3D cartoon-style and featuring hybrid cultural symbolism that blend traditional and modern elements. Based on this result, 12 participants were selected for semi-structured interviews to extract key visual elements, which were then used to construct prompts for generating a Chinese-style digital human using AIGC techniques. Eye-tracking data (N = 21) reveal that participants focused primarily on facial region and culturally symbolic costume details, such as jewelry, embroidery, and ornamental fastenings. The findings highlight the potential of integrating user insight with AIGC to create culturally expressive digital humans aligned with contemporary youth aesthetics. The study offers empirical evidence and design strategies for the innovative digital expression and dissemination of traditional Chinese culture.

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Aesthetic Preference Patterns in Chinese-Style Digital Human Design: An Eye-Tracking Study with AIGC-Driven Cultural Innovation Implications

  • 会议论文

  • 发表日期：

    2026年

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  • Yuxin Sheng,
  • Feifei Chen,
  • Luyu Jiang,
  • Huinan Liu

摘要

This study investigates aesthetic preferences and visual attention patterns toward Chinese-style digital humans in the context of AI-generated content (AIGC). Combining a questionnaire (N = 136), semi-structured interviews, and eye-tracking experiments, the research identifies key design elements preferred by young consumers. Results show a strong preference for young female digital humans with extroverted personality traits, rendered in 3D cartoon-style and featuring hybrid cultural symbolism that blend traditional and modern elements. Based on this result, 12 participants were selected for semi-structured interviews to extract key visual elements, which were then used to construct prompts for generating a Chinese-style digital human using AIGC techniques. Eye-tracking data (N = 21) reveal that participants focused primarily on facial region and culturally symbolic costume details, such as jewelry, embroidery, and ornamental fastenings. The findings highlight the potential of integrating user insight with AIGC to create culturally expressive digital humans aligned with contemporary youth aesthetics. The study offers empirical evidence and design strategies for the innovative digital expression and dissemination of traditional Chinese culture.