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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.11048 |
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| _version_ | 1866912962019590144 |
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| author | Hong, Susung Curless, Brian Kemelmacher-Shlizerman, Ira Seitz, Steve |
| author_facet | Hong, Susung Curless, Brian Kemelmacher-Shlizerman, Ira Seitz, Steve |
| contents | We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting with character references, the system employs a population of agents loosely based on real production studio roles, structured to optimize the quality and diversity of ideas and outputs through iterative competition, evaluation, and improvement. A key contribution is the introduction of LLM critics aligned with real viewer preferences through the analysis of a corpus of comedy videos on YouTube to automatically evaluate humor. Our experiments show that our framework produces results approaching the quality of professionally produced sketches while demonstrating state-of-the-art performance in video generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11048 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | COMIC: Agentic Sketch Comedy Generation Hong, Susung Curless, Brian Kemelmacher-Shlizerman, Ira Seitz, Steve Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Multiagent Systems Neural and Evolutionary Computing We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting with character references, the system employs a population of agents loosely based on real production studio roles, structured to optimize the quality and diversity of ideas and outputs through iterative competition, evaluation, and improvement. A key contribution is the introduction of LLM critics aligned with real viewer preferences through the analysis of a corpus of comedy videos on YouTube to automatically evaluate humor. Our experiments show that our framework produces results approaching the quality of professionally produced sketches while demonstrating state-of-the-art performance in video generation. |
| title | COMIC: Agentic Sketch Comedy Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Multiagent Systems Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2603.11048 |