This paper introduces a media service model that exploits artificial
intelligence (AI) video generators at the receive end. This proposal deviates
from the traditional multimedia ecosystem, completely relying on in-house
production, by shifting part of the content creation onto the receiver. We
bring a semantic process into the framework, allowing the distribution network
to provide service elements that prompt the content generator, rather than
distributing encoded data of fully finished programs. The service elements
include fine-tailored text descriptions, lightweight image data of some
objects, or application programming interfaces, comprehensively referred to as
semantic sources, and the user terminal translates the received semantic data
into video frames. Empowered by the random nature of generative AI, the users
could then experience super-personalized services accordingly. The proposed
idea incorporates the situations in which the user receives different service
providers' element packages; a sequence of packages over time, or multiple
packages at the same time. Given promised in-context coherence and content
integrity, the combinatory dynamics will amplify the service diversity,
allowing the users to always chance upon new experiences. This work
particularly aims at short-form videos and advertisements, which the users
would easily feel fatigued by seeing the same frame sequence every time. In
those use cases, the content provider's role will be recast as scripting
semantic sources, transformed from a thorough producer. Overall, this work
explores a new form of media ecosystem facilitated by receiver-embedded
generative models, featuring both random content dynamics and enhanced delivery
efficiency simultaneously.

介绍了一种利用人工智能 (AI) 视频生成器在接收端的媒体服务模型，通过将部分内容创作转移到接收方，借助语义处理，将分布网络提供的服务元素作为语义源促使内容生成器，并将接收到的语义数据转换为视频帧，实现超个性化服务。该研究探索了一种由接收端嵌入生成模型支持的新型媒体生态系统，同时具有随机内容动态和提高传递效率的特点。