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This study explores the use of composite template learning methods to enhance movie poster design. By analyzing a curated dataset of movie posters, compositional elements such as object layout and scene arrangement were extracted and used to train a template-learning model for generating new posters. The results demonstrate that the proposed approach effectively captures key design features, providing realistic posters with enhanced visual coherence, genre-specific consistency, and scalability. This method offers valuable insights into AI-driven design automation for the film industry.

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Movie Poster Design Based on Composite Template Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Can Zhong,
  • Lilu Huang,
  • Wenbo Yuan,
  • Liaoruxing Zhang,
  • Chenyu Fan,
  • Xin Jin

摘要

This study explores the use of composite template learning methods to enhance movie poster design. By analyzing a curated dataset of movie posters, compositional elements such as object layout and scene arrangement were extracted and used to train a template-learning model for generating new posters. The results demonstrate that the proposed approach effectively captures key design features, providing realistic posters with enhanced visual coherence, genre-specific consistency, and scalability. This method offers valuable insights into AI-driven design automation for the film industry.