Curving spontaneously: Sougwen Chung’s collaborative paintings with algorithmic systems

Abstract

This paper examines an artistic paradigm that reinvigorates embodied labor and spontaneous mark-making in human interactions with algorithmic systems. It focuses on the curve as a central method in Sougwen Chung’s collaborative paintings with her five generations of Drawing Operations Units. Using techniques such as supervised learning, computer vision, biofeedback, and full-body tracking, Chung transforms neural activity, physical gestures, and archival drawings into data inputs that guide robotic systems to paint on canvas. Departing from the algorithmic principles of early computer art, Chung’s varying curves improvise fluid forms rather than fixed ones to accompany robotic painting gestures in real-time. The resulting abstract, textured, and spontaneous brushstrokes merge human and machine traces, challenging the assumption that computational aesthetics must appear calculated. Drawing on posthumanism concepts of ‘technosymbiosis’ and ‘cognitive assemblage,’ this paper argues that Chung reconfigures the human-algorithm relationship not as a question of control, but as a dialogic process and embodied communion.