Cross-modal film and television content generation and dynamic modeling of ethical risks based on diffusion model-federated learning collaborative optimization
Guang Xing · Discover Artificial Intelligence · 2025
Cross-modal film and television content generation is an important direction of the integration of artificial intelligence and multimedia technology, which has promoted the development of intelligent film and television production, virtual reality, digital entertainment, and other fields. The generative model based on deep learning [ 1 , 2 ] shows high visual quality and creative expression ability in the conversion of text to video, but there are still problems such as inter-frame inconsistency, semantic shift, and violation of physical laws, which affect the coherence of long-time video. As a powerful generation method, the diffusion model [ 3 , 4 ] shows excellent detail restoration ability in the task of video generation [ 5 , 6 ]. However, because it mainly relies on frame-level generation and lacks global modeling of the time dimension [ 7 , 8 ], it leads to poor continuity between adjacent frames, and unreasonable changes in character form, action trajectory, background changes, etc., are prone to occur. Cross-modal film and television content generation technology has made significant progress in recent years. The research focuses on the generation methods constrained by conditions such as text and audio, and explores how to improve the alignment effect between different modalities to optimize the generation quality and semantic consistency. Related research systematically reviews the latest progress in cross-modal visual content generation and fills the gap in audio dataset compilation. In-depth analysis is provided in terms of modal alignment, feature fusion, and multimodal collaborative modeling, and the future development trend of multimodal fusion is discussed [ 9 ]. For the problem of music recommendation for user-generated micro-videos, some studies have proposed a de-confusion cross-modal matching model based on backdoor adjustment and Monte Carlo estimation. Combining the teacher-student network with knowledge transfer technology, it reduces the bias in music selection and improves the rationality of recommendation through the collaborative optimization of professional content and user historical preferences [ 10 ]. In the field of multimodal sentiment analysis, to optimize the fusion effect of audio and video features, researchers construct a video-based cross-modal auxiliary network. By enhancing the diversity of audio features, filtering redundant visual frames, and combining classifier groups for sentiment prediction, the classification accuracy is significantly improved [ 11 ]. Although the above methods have made some progress in cross-modal generation and optimization, there is still room for improvement in the robustness of long-term modeling, content consistency guarantee, and cross-modal semantic alignment.