Workshop practices

This article examines a selection of paintings by Guido Reni (1575–1642) and his studio that display comparable stylistic, compositional, and technical characteristics, all of which have been ascribed dates loosely between 1620 and 1630. The works that form the primary focus of discussion include the recently restored Toilet of Venus at the National Gallery, London, Joseph and Potiphar’s Wife from the collection of the J. Paul Getty Museum, Los Angeles, two versions of the Penitent Magdalene—one in a private collection, the other a later work in the Galleria Nazionale d’Arte Antica in Rome—and Venus and Cupid from the Toledo Museum of Art, Ohio. By means of visual analysis and, where available, the study of technical imaging, links between these works become apparent, which, in turn, provide insight into Reni’s production process and studio practices during this period of his career.

Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a chromatic confocal optical profilometer to produce surface height data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of square patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60–96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, even as small as a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice.

Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a confocal optical profilometer to produce surface data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60 to 96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, as small as twice a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice.

Maria Aresin, Bleeding Paper: The Use of Red Oil Paint in Drawings from Tintoretto’sWorkshop

Amongst the drawings by Tintoretto and his workshop there are some twenty sheets showing figure studies on the recto and tracings made with reddish brown oil paint of the same figure on the verso. Sometimes these tracings appear again on the recto, covering the black chalk contour lines of the body shapes with pasty paint. Only the outlines are rendered or copied as if to create a plain stencil of the figures position without highlighting or adding also the inner body volumes or muscles. It seems that no other artist at the time in Venice used this particular technique. Until now, its function has been unclear, particularly because although these drawings are kept in famous drawing collections (Uffizi, Morgan Library, Museo Capodimonte), they have yet to be studied. This paper will shed light on Tintoretto’s use of oil paint on blue or buff brown paper as a method of mirroring the figure to create new possibilities and adapt the postures to many paintings at the same time, thus recycling his figure studies for different purposes. Understanding that process of tracing, reversing and adapting the figure helps to better understand the process of preparing a painting in the Tintoretto workshop.

Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a chromatic confocal optical profilometer to produce surface height data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of square patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60–96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, even as small as a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice.

New technology makes man change habits and allows new ones ways to do things. Digital 3D printing opens up ways to create objects where hybrids and transitional forms are materialized symbols on a new era. With natural materials, it shows a new way of dealing with the world at where they are traditional creative rituals are change. In the artistic exploration, the camp becomes natural geological processes processed through digital algorithms. Like the alchemist experimenting with materials to create gold, we explore such 3D print in clay to create new art prints. The project was exhibited at Gallery Bergman, Karlstad, Sweden, from 15th to 22nd of June 2017.



Arild Berg, Norway (Project Leader / Artist, Researcher)
Sigrid Haugen, Norway (Designer, Researcher)
Kristin Andreassen, Norway (Artist, Researcher)
Ozgu Gundeslioglu, Turkey (Artist, PhD Student)

Nenad Pavel, Norway (Designer, PhD Student)
Mikael Omlid, Norway (Engineer)
Jon Samseth, Norway (Professor of Physics, Researcher)

Uhrmas Puhkan, Estonia (Artist, Researcher)