Controllable Generation of Yangtze River Aquatic Organism-Themed Virtual Humans Based on Semantic Mapping of Human Attribute Features

Existing virtual human generation methods often inadequately preserve individual attributes, lack coherent visual-semantic mapping, and insufficiently integrate regional cultural features. To address these limitations, this study proposes a controllable generation method combining human attribute recognition, visual-semantic mapping, and conditional image generation, using visual features of Yangtze River aquatic organisms as the application context. The system recognizes age group, gender, clothing category, and clothing colour from an input human image and encodes these attributes into a structured representation. A visual-semantic mapping mechanism then links human attributes with fish-specific colour, texture, and morphological features, converting them into structured generation descriptions. These descriptions guide a generative model to produce mermaid-like virtual characters that retain key human attributes while incorporating distinctive fish-inspired visual elements. Experimental results indicate stable performance in attribute consistency, structural integrity, visual-style controllability, and applicability to cultural tourism scenarios. The proposed method establishes an interpretable and extensible pipeline from attribute recognition to cross-domain semantic translation and conditional generation, offering a practical approach to personalized virtual character creation and a methodological reference for the digital presentation of regional ecological and cultural resources.