Descendent: Understanding the Digital Production Process From Human Interpretation to Algorithmic Interpolation to AI Inference

Research output: Chapter in book/report/conference proceedingChapterpeer-review

Abstract

This chapter examines the transition from human interpretation, to algorithmic interpolation, to AI inference in digital creative processes. Using the case study of an AI-driven 3D avatar, it examines three junctures in the production pipeline where AI inference is replacing human interpretation and algorithmic interpolation: the cleaning and reposing of motion data, the rigging of a virtual avatar, and the denoising of the rendered output. In each case, human interpretation and algorithmic filtering are being replaced by an inherently conservative process of AI inference, where the outputs are driven by conventions established in the dataset. Following actor Andy Serkis’s description of computer graphics artists doing ‘digital make-up’, this chapter examines AI beyond reductive notions of optimization and connects them to broader debates in digitally-augmented performance, specifically how the qualitative differences between interpretation, interpolation, and inference can help artists understand how to orient themselves antagonistically towards their tools.
Original languageEnglish
Title of host publicationChoreomata: Performance and Performativity after AI
EditorsRoberto Alonso Trillo, Marek Poliks
Place of PublicationLondon; New York
PublisherChapman and Hall/CRC
Chapter19
Pages467-481
Number of pages15
Edition1st
ISBN (Electronic)9781003312338
ISBN (Print)9781032319919, 9781032319988
DOIs
Publication statusPublished - 20 Dec 2023

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