Restoration Speedrun: MIT Student Prints AI-Polymer Masks to Restore Paintings in Hours
A team of researchers at the Massachusetts Institute of Technology (MIT) has made a groundbreaking discovery that could revolutionize the field of art conservation. Graduate student Alex Kachkine, who spent nine months restoring a damaged baroque Italian painting, has developed a technique that uses AI-generated polymer films to physically restore damaged paintings in hours rather than months.
The innovative method involves printing transparent "masks" containing thousands of precisely color-matched regions that conservators can apply directly to an original artwork. Unlike traditional restoration, which permanently alters the painting, these masks can reportedly be removed whenever needed. This reversible process does not change a painting’s original state, making it a significant breakthrough in art conservation.
According to Kachkine, his method works by creating a digital record of what mask was used during restoration. In 100 years, when someone else is working with the same painting, they will have an extremely clear understanding of what was done to the artwork. This is unprecedented in conservation and has the potential to transform the way art institutions approach preservation.
The Problem: Art Collections Hiding in Plain Sight
Nature reports that up to 70 percent of institutional art collections remain hidden from public view due to damage. Traditional restoration methods, which require conservators to painstakingly fill damaged areas one at a time while mixing exact color matches for each region, can take weeks to decades for a single painting. The lack of skilled conservators means that many artworks are left untouched and unseen.
Kachkine’s idea was born during a 2021 cross-country drive to MIT, where he visited galleries and realized how much art remains hidden due to damage and restoration backlogs. As someone who restores paintings as a hobby, he understood both the problem and the potential for a technological solution.
The Restoration Process: From Scanning to Printing
To demonstrate his method, Kachkine chose a challenging test case: a 15th-century oil painting requiring repairs in 5,612 separate regions. An AI model identified damage patterns and generated 57,314 different colors to match the original work. The entire restoration process reportedly took 3.5 hours—about 66 times faster than traditional hand-painting methods.
The mechanical engineering student avoided using generative AI models like Stable Diffusion or full-area application of GANs for digital restoration. These models cause spatial distortion that would prevent proper alignment between the restored image and the damaged original. Instead, Kachkine utilized computer vision techniques found in prior art conservation research: cross-applied coloration for simple damages and local partial convolution for reconstructing low-complexity patterns.
From Pixels to Polymers
Kachkine’s process begins conventionally enough, with traditional cleaning to remove any previous restoration attempts. After scanning the cleaned painting, the aforementioned algorithms analyze the image and create a virtual restoration that predicts what the damaged areas should look like based on the surrounding paint and the artist’s style.
The innovative part is what happens next: custom software maps every region needing repair and determines the exact colors required for each spot. This information is then translated into a two-layer polymer mask printed on thin films—one layer provides color, while a white backing layer ensures the full color spectrum reproduces accurately on the painting’s surface. The two layers must align precisely to reproduce colors accurately.
High-fidelity inkjet printers produce the mask layers, which Kachkine aligns by hand and adheres to the painting using conservation-grade varnish spray. Importantly, the polymer materials dissolve in standard conservation solutions, allowing future removal of the mask without damaging the original work. Museums can also store digital files documenting every change made during restoration, creating a paper trail for future conservators.
The Future of Art Conservation
Kachkine says that his technology doesn’t replace human judgment—conservators must still guide ethical decisions about how much intervention is appropriate and whether digital predictions accurately capture the artist’s original intent. "It will take a lot of deliberation about the ethical challenges involved at every stage in this process to see how can this be applied in a way that’s most consistent with conservation principles," he told MIT News.
For now, the method works best with paintings that include numerous small areas of damage rather than large missing sections. In a world where AI models increasingly seem to blur the line between human- and machine-created media, it’s refreshing to see a clear application of computer vision tools used as an augmentation of human skill and not as a wholesale replacement for the judgment of skilled conservators.
Conclusion
The development of AI-printed film technology has the potential to revolutionize art conservation. By providing a reversible process that does not permanently change a painting, Kachkine’s method opens up new possibilities for preserving cultural heritage. While there are still many challenges to overcome, this breakthrough marks an important step forward in the field of art conservation and may one day make it possible to restore damaged paintings more quickly and accurately than ever before.
References
- Kachkine, A. (2025). Artificial Intelligence-Generated Polymer Films for Physical Restoration of Damaged Paintings. Nature.
- MIT News. (2025). Graduate Student Develops AI-Polymer Masks to Restore Paintings in Hours.