Hector Rodriguez

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Most Recent Affiliation:


  • City University of Hong Kong

Location:


  • Hong Kong

Bio:

  • Hector Rodriguez is a Hong Kong-based digital artist and theorist whose work explores the unique possibilities of computational technologies to reconfigure the history and aesthetics of moving images. He received a commendation award from the Hong Kong Government for his contributions to art and culture in 2014. He was awarded the Best Digital Work in the Hong Kong Art Biennial 2003, an Achievement Award at the Hong Kong Contemporary Art Awards 2012, and the Jury Selection Award of the Japan Media Art Festival 2012. His works have been internationally exhibited in Taiwan, Singapore, US, Poland, Germany, Spain, Greece, France, London, and more. His recent exhibitions include the 15th &16th WRO Media Art Biennale, Poland (2013, 2015), “European Conference on Computer Vision (2018), A.I. Art Gallery, Conference on Neural Information Processing System (2018, Montreal), RIXC Art Science Festival (2017, Riga), Generative Art Conference/Exhibition (2017), Athens Media Art Festival (2018, Greece), xCoAx conference on computation and art (2016, GAMEC, Bergamo), CyNetArt competition (2016 Dresden) and many more. Recently, he had his solo retrospective “Hidden Variables” in Hong Kong, October 2018. He was the Artistic Director of the Microwave International Media Art Festival in 2004-2006, and is Director for Research and Education for the Writing Machine Collective. He currently teaches at the School of Creative Media, City University of Hong Kong, where he founded the undergraduate program in art and science.

Writings and Presentations:


  • Title: Algorithmic Analysis and Visualization of Motion in Cinema
    Writing Type: Sketch / Art Talk
    Author(s):
    Exhibition: SIGGRAPH Asia 2019: Deep Dreaming
    Abstract Summary:

    This talk explores some of the possible applications of unsupervised machine learning methods in found footage cinema, a tradition of experimental art that re-edits excerpts from existing films. This artistic practice sometimes aims to reconfigure our experience of the moving image heritage. In this context, machine learning algorithms has the potential to capture aspects of the cinematic experience for which we lack critical concepts, and which are for this reason difficult to describe. One important example concerns cinematic motion. Established critical discourse often speaks of motion in film by reference to the movement of objects or the camera. Film scholars might describe a scene by noting, for instance, that a person is walking fast or that the camera is tilting upwards. What is missing in this kind of description is the visual texture of cinematic movement. The two-channel algorithmic installation Errant: The Kinetic Propensity of Images applies matrix factorization techniques to the analysis of optical flow in cinema, focusing on the work of Chinese director King Hu. This method produces a visual dictionary of basic motion patterns the represent what could be described as the “kinetic overtones” of image sequences. The results are then visualized using streaklines, a technique from fluid dynamics. This presentation will discuss the motivation and methodology used in the production of this work, in relation to other work by the speaker. Implications for cinema theory will also be briefly discussed.

    Role(s):