Abstract
Online content posted by amateur content creators may not be of sufficient quality to drive engagement. For example, such content may have audio or video glitches. Viewer feedback can indicate the problems with user-generated content. This disclosure describes techniques that analyze viewer comments and/or the user-generated content to generate prompts for a generative AI model. With permission from content creators, the capability of generative artificial intelligence models to execute audio-enhancement tasks such as volume equalization, noise reduction, audio filtering, etc. and video-related tasks such as video smoothing, video stabilization, etc.is utilized via the generated prompts to automatically improve their content. The content creator has control over the versions generated prior to release and can specify the types of edits as well as the portions of content that are modified. Automatic editing of content can help creators that are unskilled or lack access to advanced tools to improve the quality of their content and can improve the viewer experience of platforms that host such content.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sriramachandran, Jaunani and Sedouram, Ramprasad, "Generative AI Driven Content Quality Enhancement for User-generated Content", Technical Disclosure Commons, (December 11, 2023)
https://www.tdcommons.org/dpubs_series/6486