Guiding AI-Generated Digital Content with Wireless Perception
- URL: http://arxiv.org/abs/2303.14624v1
- Date: Sun, 26 Mar 2023 04:39:03 GMT
- Title: Guiding AI-Generated Digital Content with Wireless Perception
- Authors: Jiacheng Wang, Hongyang Du, Dusit Niyato, Zehui Xiong, Jiawen Kang,
Shiwen Mao, and Xuemin (Sherman) Shen
- Abstract summary: We introduce an integration of wireless perception with AI-generated content (AIGC) to improve the quality of digital content production.
The framework employs a novel multi-scale perception technology to read user's posture, which is difficult to describe accurately in words, and transmits it to the AIGC model as skeleton images.
Since the production process imposes the user's posture as a constraint on the AIGC model, it makes the generated content more aligned with the user's requirements.
- Score: 69.51950037942518
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in artificial intelligence (AI), coupled with a surge in
training data, have led to the widespread use of AI for digital content
generation, with ChatGPT serving as a representative example. Despite the
increased efficiency and diversity, the inherent instability of AI models poses
a persistent challenge in guiding these models to produce the desired content
for users. In this paper, we introduce an integration of wireless perception
(WP) with AI-generated content (AIGC) and propose a unified WP-AIGC framework
to improve the quality of digital content production. The framework employs a
novel multi-scale perception technology to read user's posture, which is
difficult to describe accurately in words, and transmits it to the AIGC model
as skeleton images. Based on these images and user's service requirements, the
AIGC model generates corresponding digital content. Since the production
process imposes the user's posture as a constraint on the AIGC model, it makes
the generated content more aligned with the user's requirements. Additionally,
WP-AIGC can also accept user's feedback, allowing adjustment of computing
resources at edge server to improve service quality. Experiments results verify
the effectiveness of the WP-AIGC framework, highlighting its potential as a
novel approach for guiding AI models in the accurate generation of digital
content.
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