School of Visual Arts

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Browsing School of Visual Arts by Author "Duer, Zachary"

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    dAnCing LiNes
    Emanuele, Ella; Hunter, David; Duer, Zachary; Birch, Simon (ACM, 2023-06-19)
    How do we interpret a multi-participant choreographed performance in the public domain through digital technologies? In collaboration with data visualisation expert David Hunter from University of Colorado at Boulder, and visual artist Zach Duer from Virginia Tech, dAnCing LiNes explores how dance can generate a choreographic view of drawing through mediated representation. In this respect the artwork produced for dAnCing LiNes is not intended as a means of documentation of the live events but as a tool for new artistic production. The intention is to rethink performative drawing beyond the gestural trace of the body in movement through the use of data visualisations. Capturing chorographic scores and task-based instructions through digital technologies, the data visualisations explore how the agency of dance moves from the performative to the visual via technological means by using combinations of established computer vision techniques from OpenCV [1] like Optical Flow, Blob Detection. The visualisations not only reveal the rules of the underlying choreography in each location but also computationally play with and exemplify those rules on a per location basis (five in total).
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    Echofluid: An Interface for Remote Choreography Learning and Co-creation Using Machine Learning Techniques
    Wang, Marx; Duer, Zachary; Hardwig, Scotty; Lally, Sam; Ricard, Alayna; Jeon, Myounghoon (ACM, 2022-10-29)
    Born from physical activities, dance carries beyond mere body movement. Choreographers interact with audiences’ perceptions through the kinaesthetics, creativity, and expressivity of whole-body performance, inviting them to construct experience, emotion, culture, and meaning together. Computational choreography support can bring endless possibilities into this one of the most experiential and creative artistic forms. While various interactive and motion technologies have been developed and adopted to support creative choreographic processes, little work has been done in exploring incorporating machine learning in a choreographic system, and few remote dance teaching systems in particular have been suggested. In this exploratory work, we proposed Echofuid-a novel AI-based choreographic learning and support system that allows student dancers to compose their own AI models for learning, evaluation, exploration, and creation. In this poster, we present the design, development and ongoing validation process of Echofluid, and discuss the possibilities of applying machine learning in collaborative art and dance as well as the opportunities of augmenting interactive experiences between the performers and audiences with emerging technologies.