Film posters serve as front-end visual communication media that shape viewers’ initial judgments of film genre, emotional tone, and viewing appeal. However, whether the optimal overall color configuration follows a universal rule or varies across poster types remains insufficiently examined. This study investigated how overall lightness and chroma influence the communication effects of film posters and identified type-specific optimal color intervals. Based on a cross-type poster sample library, film posters were classified into four visual grammar types: affable-entertaining, relational-emotional, spectacle-dynamic, and threat-suspenseful. Type-specific quantile thresholds for lightness and chroma were established within each category. Eye-tracking data, subjective ratings, mixed-effects response surface modeling, and constrained desirability optimization were combined to identify optimal regions of overall color configuration. The results show that no single optimal lightness–chroma interval applies across all poster types. The dominant optimal interval was low lightness–high chroma for affable-entertaining and relational-emotional posters, high lightness–low chroma for spectacle-dynamic posters, and medium lightness–high chroma for threat-suspenseful posters. These findings indicate that overall color optimization varies across poster types within the present experimental context and provide practical support for evidence-based, type-specific poster color design.

Contemporary dynamic interface designs for consumer IoT devices are constrained by several pressing issues, including excessive homogenization, insufficient real-time adaptability, and low generative efficiency. These constraints limit the ability to meet growing user expectations for personalized interactions and high-fidelity virtual experiences. To address these issues, this study proposes Puff-Net, an intelligent generation framework that overcomes such limitations through the synergistic integration of fractal algorithms and neural style transfer. First, multi-scale artistic textures are created by exploiting fractal self-similarity, while an end-to-end aesthetic control is achieved using a dynamic optimization module that incorporates gradient constraints and fractal dimension regularization. Second, a dual-branch feature architecture is designed to separately extract device state information and user preference style features, which are adaptively recombined to enable the dynamic visual expression of a holographic avatar. Finally, the framework ensures privacy and security while meeting the stringent requirements for multi-resolution display and real-time interaction in Internet of Things scenarios. Experimental results indicate that the proposed framework effectively enables dynamic interface generation across applications such as smart home systems and wearable devices. Through the coordinated use of fractal generation and style transfer, Puff-Net surpasses the limitations of traditional template-based design approaches, offering a lightweight solution that harmonizes artistic expression with functional responsiveness. Overall, this study presents a novel theoretical and technical foundation for integrating holographic representations within the IoT ecosystem, paving the way for the next generation of intelligent and personalized user interfaces in consumer electronics.