The intelligent development and preservation of folk sports culture under artificial intelligence

To promote the intelligent development and preservation of folk sports culture, this work proposes a model grounded in the Cycle-Consistent Generative Adversarial Network (CycleGAN) to produce high-quality human images that recreate traditional sports movements. In order to improve the performance of the model, a discriminative mechanism for pose consistency and identity consistency is innovatively designed, and an appearance consistency loss function is introduced. Finally, the effectiveness of the model in image generation is verified. Experiments conducted on the DeepFashion and Market-1501 datasets suggest that compared to other models, the proposed model achieves superior visual quality and realism in the generated images. In ablation experiments, the model incorporating the appearance consistency loss achieves improvements of 1.49%, 1.76%, and 2.2% in image inception score, structural similarity index, and diversity score, respectively, compared to the best-performing comparative models. This demonstrates the effectiveness of this loss function in improving image quality. Moreover, the proposed model excels across multiple evaluation metrics when compared to other models. In authenticity discrimination experiments, the generated images have a 58.25% probability of being judged as real, significantly surpassing other models. In addition, the results on the folk sports culture action dataset also show that the model proposed performs excellently in multiple indicators, and it particularly has an advantage in the balance between image diversity and quality. These results indicate that the CycleGAN model better reproduces the details and realism of folk sports movements. This finding provides strong technical support for the digital preservation and development of traditional sports culture.

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