Character Generation Powered by Multimodal Foundation Models: Exploring Semantic Control and Visual Consistency Mechanisms

In this paper, a novel character generation system based on a multi-modal model is developed to achieve precise semantic control through the deep integration of visual features and text description. The system adopts a two-channel data processing architecture, the image generation module relies on a convolutional network to analyze physical features, and the text understanding module uses an attention mechanism to extract character attributes. The innovative design of the semantic matching loss function and the cross-consistency constraint mechanism effectively solves the disconnection problem between modeling features and setting descriptions in traditional methods. In the game character generation test, this system has better semantic restoration and multi-perspective image stability than the baseline model. Typical case verification shows that when the compound role parameter is input, the generated result can accurately present the corresponding relationship between clothing details and personality features, and maintain the consistency of modeling elements under different action states. This multimodal collaborative framework offers a new technical avenue for creating virtual characters, making digital images narrative depth and visual expression.