Abstract In order to improve the temporal consistency problem in cross-modal film and television content generation and the generalization problem in the dynamic modeling of ethical risks, this paper proposes an optimization strategy based on a diffusion model, combined with temporal Transformer and neural radiant field (NeRF) constraints, and constructs a dynamic modeling framework for ethical risks based on federated learning. Temporal Transformer is used for inter-frame modeling to improve the temporal consistency of video generation; combined with the spatial structure of NeRF constraints, a physical guidance mechanism is applied to ensure that the character actions and environmental changes conform to physical laws. In the federated learning framework, gradient pruning and quantization techniques are used to reduce communication overhead, and self-supervised contrastive learning is used to optimize cross-modal ethical risk detection. By combining explainable artificial intelligence (AI), key frame heat maps and ethical decision trees are generated to improve the transparency of risk assessment. Experimental results show that the temporal consistency scores of the proposed method in short videos and long videos are improved to 0.90 and 0.88 respectively, which are about 29% and 35% higher than the baseline method; after NeRF constraint optimization, the structural similarity index (SSIM) at 90° viewing angle is improved from 0.55 to 0.78, and the peak signal-to-noise ratio (PSNR) is improved from 18 to 27 dB, which significantly improves the spatial consistency; in ethical risk detection, after 50 rounds of training, the classification accuracy of violent content, discriminatory content and false information of the model combining self-supervised learning and federated learning is improved to 96.8%, 96.1% and 94.7% respectively, which is significantly better than the convolutional neural network (CNN) method. While ensuring the quality of film and television content generation, the proposed method realizes ethical risk control and constructs a safe and controllable cross-modal film and television content generation system. Article highlights <UnorderedList Mark="Bullet"> <ItemContent> Technological Innovation: A novel method combining diffusion models and federated learningenhances cross-modal film and television content generation, achieving superior temporal andspatial consistency with notable performance improvements in SSIM and PSNR metrics. </ItemContent> <ItemContent> Ethical Risk Management: Integration of self-supervised learning and federated learning createsan effective ethical risk modeling framework, significantly boosting detection accuracy for violent,discriminatory, and false information while enhancing assessment transparency throughinterpretable AI. </ItemContent> <ItemContent> Practical Implications: The study advances the quality and safety of film and television contentgeneration, offering a robust framework for ethical control. Future directions include leveraging large-scale pre-trained models and dynamic ethical mechanisms to further refine intelligent filmand television technologies. </ItemContent> </UnorderedList>

Cross-modal film and television content generation and dynamic modeling of ethical risks based on diffusion model-federated learning collaborative optimization

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发表日期：

2025年9月30日

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Guang Xing

摘要

Abstract

In order to improve the temporal consistency problem in cross-modal film and television content generation and the generalization problem in the dynamic modeling of ethical risks, this paper proposes an optimization strategy based on a diffusion model, combined with temporal Transformer and neural radiant field (NeRF) constraints, and constructs a dynamic modeling framework for ethical risks based on federated learning. Temporal Transformer is used for inter-frame modeling to improve the temporal consistency of video generation; combined with the spatial structure of NeRF constraints, a physical guidance mechanism is applied to ensure that the character actions and environmental changes conform to physical laws. In the federated learning framework, gradient pruning and quantization techniques are used to reduce communication overhead, and self-supervised contrastive learning is used to optimize cross-modal ethical risk detection. By combining explainable artificial intelligence (AI), key frame heat maps and ethical decision trees are generated to improve the transparency of risk assessment. Experimental results show that the temporal consistency scores of the proposed method in short videos and long videos are improved to 0.90 and 0.88 respectively, which are about 29% and 35% higher than the baseline method; after NeRF constraint optimization, the structural similarity index (SSIM) at 90° viewing angle is improved from 0.55 to 0.78, and the peak signal-to-noise ratio (PSNR) is improved from 18 to 27 dB, which significantly improves the spatial consistency; in ethical risk detection, after 50 rounds of training, the classification accuracy of violent content, discriminatory content and false information of the model combining self-supervised learning and federated learning is improved to 96.8%, 96.1% and 94.7% respectively, which is significantly better than the convolutional neural network (CNN) method. While ensuring the quality of film and television content generation, the proposed method realizes ethical risk control and constructs a safe and controllable cross-modal film and television content generation system.

Article highlights

Technological Innovation: A novel method combining diffusion models and federated learningenhances cross-modal film and television content generation, achieving superior temporal andspatial consistency with notable performance improvements in SSIM and PSNR metrics.

Ethical Risk Management: Integration of self-supervised learning and federated learning createsan effective ethical risk modeling framework, significantly boosting detection accuracy for violent,discriminatory, and false information while enhancing assessment transparency throughinterpretable AI.

Practical Implications: The study advances the quality and safety of film and television contentgeneration, offering a robust framework for ethical control. Future directions include leveraging large-scale pre-trained models and dynamic ethical mechanisms to further refine intelligent filmand television technologies.