This study delves into the innovative application of Artificial Intelligence (AI) and machine learning algorithms in the realm of ethnic fashion design, with a specific emphasis on the Miao women's apparel. We introduce an AI-powered approach that strategically bridges modern technology with traditional elements, denoting a significant stride in the field of fashion design. Our research underscores three major aspects: customization of body shape, fabric selection, and innovative design. An AI-driven statistical methodology was utilized to accurately adapt to the unique body characteristics of Miao women, demonstrating an application of machine learning in pattern recognition. Furthermore, the AI's capacity to analyze the fabric properties was harnessed to optimize material selection, creating a balance between aesthetics and comfort. The innovative use of the Multimodal Unsupervised Image-to-Image Translation (MUNIT) algorithm, an AI tool, generated diverse and trendy designs, thereby enriching the distinctiveness of ethnic apparel. Our study accentuates the synergistic blend of traditional crafting methods and modern technological applications, highlighting the role of AI in the sustainable development of ethnic fashion. Additionally, we also demonstrate the advantages of Made-to-Measure (MTM) approaches, emphasizing the importance of individual customization in contemporary fashion design. This research presents a pioneering exploration at the nexus of AI, pattern recognition, and ethnic fashion design, which has the potential to transform the future of the fashion industry.

In recent years, the field of artificial intelligence (AI) has witnessed remarkable progress and its applications have extended to the realm of video games. The incorporation of AI in video games enhances visual experiences, optimizes gameplay and fosters more realistic and immersive environments. In this review paper, we systematically explore the diverse applications of AI in video game visualization, encompassing machine learning algorithms for character animation, terrain generation and lighting effects following the PRISMA guidelines as our review methodology. Furthermore, we discuss the benefits, challenges and ethical implications associated with AI in video game visualization as well as the potential future trends. We anticipate that the future of AI in video gaming will feature increasingly sophisticated and realistic AI models, heightened utilization of machine learning and greater integration with other emerging technologies leading to more engaging and personalized gaming experiences.

This review explores the integration of human posture recognition and classification technologies in performing arts education, focusing on the advancements in deep learning, neural networks, and computer vision. It traces the evolution of these technologies and highlights their significance in developing personalized teaching methods for performing arts. Following this, we examine various posture recognition technologies, emphasizing data acquisition and processing methods while revealing their strengths and limitations, particularly in their adaptability to performing arts. Moreover, the review discusses posture classification methods, addressing the processing and interpretation of data, and the challenges in achieving classification accuracy and efficiency. Illustrative case studies demonstrate the application of these technologies in enhancing teaching and creativity in performing arts, significantly contributing to the skill development and expressiveness of artists and students. Concluding with a critical assessment of the current feasibility of these technologies in performing arts education, the review suggests potential improvements and future directions. It emphasizes the indispensable role of human posture recognition in advancing performing arts education and underscores the need for continued interdisciplinary research in this evolving field.

In this paper, we propose a novel framework that leverages Large Language Models (LLMs) and Generative Adversarial Networks (GANs) to address the challenges of cross-domain generalization in ethnic fashion design. By introducing the concept of "Digital Cousins," our approach generates culturally rich fashion designs that maintain both the symbolic integrity and geometric consistency of traditional ethnic garments. Specifically, we explore how LLMs can effectively encode the complex semantic relationships in cultural symbols and how GANs can transform these embeddings into visually coherent and geometrically plausible designs. Unlike traditional digital twin models, which require exact replication, our method allows for symbolic and geometric variations while preserving core cultural values. Our contributions are threefold: First, we propose a mathematical framework for mapping cultural symbols to a multi-dimensional semantic space using LLMs, ensuring the symbolic accuracy of generated designs. Second, we present a method to generate "Digital Cousins" of ethnic garments by employing GANs to introduce geometric transformations, enabling cross-cultural and cross-contextual adaptation. Finally, we demonstrate the robustness of our method in a series of experiments, including real-to-sim and sim-to-real tests, where we evaluate the generalization capabilities of the generated designs across different cultural contexts. Our results show that the proposed approach achieves higher cross-domain performance compared to traditional methods, while maintaining cultural authenticity. Experimental results show that our approach achieves a 15% improvement in cultural fidelity and a 20% enhancement in geometric adaptability compared to traditional methods. These findings suggest significant potential for AI-driven innovation in ethnic fashion design, with applications in both cultural preservation and modern fashion industries.

Smart virtual reality (VR) systems are becoming central to media production education, where immersive practice, real-time feedback, and hands-on simulation are essential. This review synthesizes the integration of artificial intelligence (AI) into human-centered, interactive VR learning for television and media production. Searches in Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink (2013–2024) identified 790 records; following PRISMA screening, 94 studies met the inclusion criteria and were synthesized using a systematic scoping review approach. Across this corpus, common AI components include learner modeling, adaptive task sequencing (e.g., RL-based orchestration), affect sensing (vision, speech, and biosignals), multimodal interaction (gesture, gaze, voice, haptics), and growing use of LLM/NLP assistants. Reported benefits span personalized learning trajectories, high-fidelity simulation of studio workflows, and more responsive feedback loops that support creative, technical, and cognitive competencies. Evaluation typically covers usability and presence, workload and affect, collaboration, and scenario-based learning outcomes, leveraging interaction logs, eye tracking, and biofeedback. Persistent challenges include latency and synchronization under multimodal sensing, data governance and privacy for biometric/affective signals, limited transparency/interpretability of AI feedback, and heterogeneous evaluation protocols that impede cross-system comparison. We highlight essential human-centered design principles—teacher-in-the-loop orchestration, timely and explainable feedback, and ethical data governance—and outline a research agenda to support standardized evaluation and scalable adoption of smart VR education in the creative industries.