AI-Colored Retro Films

Aniket Bera Started a Personal Project Coloring the Classic Pather Panchali

A professor at the University of Maryland, Aniket Bera started a personal project during quarantine after the COVID-19 outbreak using AI technology to color a classic Satyajit Ray film, Pather Panchali. The film uses artificial intelligence to color the scenes and Bera uploaded the 2.14 minute long video earlier in May, garnering attention globally.

He speaks about his project, “again, this is not the way to watch the movie but only an academic experiment. A lot of US professors and researchers experiment with so many old footage from the West. I wanted to try it out on something very close to my heart!" The process of incorporating colors and restoring the cinematic scenes took about 6-7 hours with no manual input required.

Image Credit: Aniket Bera

AI-colored Retro Films
Disruptive Innovation Opportunity: AI technology can be used to colorize classic films, providing a new and enhanced viewing experience.
Quarantine Projects
Disruptive Innovation Opportunity: The COVID-19 quarantine has inspired individuals to start personal projects and experiment with new technologies.
Artificial Intelligence in Film Restoration
Disruptive Innovation Opportunity: AI technology can be utilized in film restoration processes, saving time and effort compared to manual restoration methods.

Where This Applies

Film and Entertainment
Disruptive Innovation Opportunity: The film industry can leverage AI technology to enhance and restore classic films, attracting audiences with new viewing experiences.
Technology and Innovation
Disruptive Innovation Opportunity: The advancements in AI technology open up opportunities for innovation and experimentation in various industries.
Academia and Research
Disruptive Innovation Opportunity: Academics and researchers can utilize AI technology to experiment with and analyze old footage, developing new insights and discoveries.
SCORE
2.0 out of 10
GENDER
50% Men50% Women
MARKETTop markets: North America, South America, Europe, Asia, Africa
GENERATION
  • Gen Alpha
  • Gen Z (primary audience)
  • Millennial (primary audience)
  • Gen X (primary audience)
POPULARITY
Popularity 28%
Activity 24%
Freshness 9%