Tag: Glyptothek

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2nd Liminal Space at the ]a[ Research Day

On 12.11.2020 DAD’s Niko Wahl presented our intermediate results on the third research day of the Academy of Fine Arts, Vienna. Due to COVID-19, this event was an online ZOOM meeting. The goal of the ]a[ research days is to give an overview of all ongoing research projects at the academy including discussions with all participating colleagues.

Niko Wahl gave a short introduction of our project and an overview of DAD’ s collaborations with three different museums, where we work with an archive of an ethnological journal, a fine arts gallery and the statues in the Academy’s Glyptothek.

Since our work with the Austrian Museum of Folk Life and Folk Art and with the Belvedere, Vienna, has already been documented in previous blogposts [1][2], lets turn to the presentation of plaster casts at the Academy‘s Glyptothek, which we explored with Dusty, an off-the-shelf household robot.

Many people associate Artificial Intelligence (AI) with the development of ever more powerful and dextrous robots, along with horror scenarios of these machines taking over the planet. In reality robots are a small part of AI which is rather dominated by machine learning software solutions powering your Internet search engine, the natural language interface to your mobile phone, online music, movie and product recommendations and many other everyday technologies.

On the other hand, many people already own robots with limited forms of AI, for instance vacuum cleaning robots. What if we confront such a household robot with a – supposedly obsolete – museum collection of historic plaster copies of famous statues, whose very physis seems to be made of dust.

The robot takes its own route through the museum space. Following its built-in algorithms it perpetually finds new ways through the collection. It seemingly decides for itself in what order to visit the museum objects, all the time metaphorically internalizing the objects of art while inhaling their dust.

Other visitors are free to follow the robot on its path through the museum space engaging with its exhibition narrative. They might benefit form surprising relationships between objects of art established by the often creative course of the robot. Smart last generation vacuum cleaning robots are able to share their sensory experiences with others of their kind. These shared experiences usually are measurements of objects and how to avoid them when traversing a room. But what if this cloud communication, usually not accessible to us, deals with objects of art instead of everyday items? Will meeting David or the Pieta change the robots’ discourse? What if the robot meets a portrait of itself?

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Dusty visits the Glyptothek

Do you remember Dusty, the vacuum cleaner robot that explored a model version of the Glyptothek during this spring’s COVID19 related lockdown? This summer Dusty was able to experience the real Glyptothek, using its somewhat limited artificial intelligence, basically trying to avoid obstacles on its way through the maze of shelves full of plaster casts.

The Glyptothek of the Academy of Fine Arts, Vienna, is a collection
of plaster casts dating back to the late 17th century. Its main task was to serve as study material for Academy students, containing copies of a canon of world renown sculptures, ranging from plaster casts of Egyptian originals to copies of Greek and Roman, medieval, renaissance and historism statues. This collection of copies of works of art can be seen as an early analog blueprint of digital collections: the Glyptothek made the essence of European sculpture available to local audiences, who could enjoy international pieces of art without leaving their home town, very much like today‘s internet population can access digital images of the world‘s artistic heritage at the click of their handheld device.

Speaking of digital images, the above image of Dusty in the Glyptothek actually is a digital copy of an analog photograph, which in itself is an analog copy of a plaster cast which is a copy of a statue which is a copy of a real (or imagined) person …

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Case Studies

Automatic colorization of classic statues?

Automatic colorization of black and white photographs has recently been enabled by advances in machine learning (see [Zhang et al 2016] for the methods we used for the following results). Basically deep neural networks are shown millions of black and white photographs and their color versions to learn a mapping from black and white to color. After successful training, it is possible to colorize black and white photos which the machine learning algorithm has never seen before. Online services like ALGORITHMIA enable everyone to test and use this technique by simply uploading their images.

Successful colorization via ALGORITHMIA of our family’s dog Ozzy

One focus for DUST AND DATA is the Glyptothek of the Academy of Fine Arts, Vienna, which is a collection of plaster casts dating back to the late 17th century. Its main task was to serve as study material for Academy students, but it also became publicly accessible as a museum. The collection contains copies of a canon of world renown sculptures, ranging from plaster casts of Egyptian originals to copies of Greek and Roman, medieval, renaissance and historism statues. Thanks to German archaeologist Vinzenz Brinkmann, it is now an established fact that classic statues from e.g. Greek antiquity were originally painted in bright colors.

This color restoration shows what a statue of a Trojan archer from the Temple of Aphaia, Aegina would have originally looked like (CC BY-SA 2.5, from Wikipedia)

As for DUST AND DATA, all we are given are black and white photos of plaster casts from the Glyptothek. These are digital photo copies of analog plaster copies of statues. Is this sufficient to obtain any kind of meaningful result when trying to automatically colorize classic statues?

Plaster cast of dying warrior from the Temple of Aphaia, Aegina (Photo: Gemäldegalerie der Akademie der bildenden Künste Wien)
Automatic colorization via ALGORITHMIA

The automatic colorization of our dying warrior did not quite succeed, but is has many interesting features nevertheless. E.g. the algorithm did correctly “understand” that the statue is held by a real person, hence the colorization to skin tones of the person’s arm and dark color of the trousers. As for the statue, it is rendered in a light brown color, probably imitating the color of statues it has seen during training of the machine learning system. But what about the arm bottom left in the picture? It has almost a lifelike skin tone. And even more astonishing, the red bloodlike colorization of the amputed arm stump!

Plaster cast of Michelangelo’s David (Photo: Gemäldegalerie der Akademie der bildenden Künste Wien) and colorization via ALGORITHMIA

The Glyptothek also has a copy of Michelangelo’s David, or at least its head. The colorization above does provide a skinlike pink tone for the face and even blond hair.

The full David by Michelangelo (CC BY 3.0 by Wikipedia) and a colorization by ALGORITHMIA

Applying the colorization to the full David statue gives a lifelike pinkish skin tone, at least much more so than for the dying warrior above. The fact that Michelangelo’s David is such a realistic sculpture probably made it possible to trick the algorithm into treating the photo of David as the photo of a real naked person.

So can we use machine learning to automatically colorize photos of plaster casts from the Glyptothek? This would require training an algorithm with thousands of color restorations of antique statues (see photo of the Trojan archer above) to have any chance of success. But application of state-of-the-art colorization algorithms already now provides interesting results by exposing some of the biases and failures of their machine learning machinery.