Abstract
Manual post-production of multi-camera footage can be a labor-intensive process, and some existing automated solutions may have limitations in comprehending narrative context or adapting content to the cinematic principles of diverse output formats. The disclosed technology relates to a framework that can facilitate multi-format content generation by first synchronizing raw footage and extracting rich metadata, which can include information such as speaker identification, emotion, and scene segmentation. A narrative-specific agent can then utilize a knowledge base of cinematic rules to score and rank video segments according to a desired output format, for example, a movie, a social media reel, or a blooper compilation. This method may allow for the assembly of multiple, stylistically distinct, and context-aware video edits from a single set of production assets, potentially reducing manual editing time and increasing the value of raw footage.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Jang Bahadur, Sunil Kumar; Nair, Girish; Mishra, Varun; and Dhar, Gopala, "An AI Framework for Automated Generation of Multiple Content Formats from Multi-Camera Productions", Technical Disclosure Commons, (September 22, 2025)
https://www.tdcommons.org/dpubs_series/8612