Abstract In cultural heritage projects and artistic documentation, motion capture has emerged as a key archival strategy that is promised to be a “next stage” solution in preserving and accessing the past. However, motion capture is not an objective recording; it transposes a technological bodily imaginary onto the bodies whose movements it documents. This essay is situated at the intersection of current critical discourses on archives, dance and AI, bringing domain-specific knowledge to reimagine biased algorithmic systems. Although there is substantial risk for representational harms in how current AI motion models are used to render dancing bodies as data, recent projects show that retaining the entanglement of expert practitioners can refine data processing. We argue that incorporating the specificity of dance-based knowledge can support more meaningful historical research practices, in particular when understanding how bodies are themselves also archives. This contributes to identifying and countering the harms that arise from the mismatch between what automated motion extraction systems purport to accomplish and what they actually represent. The article outlines questions and guidelines that reimagine motion data through a visceral approach for an era of AI.
Bench et al. (Thu,) studied this question.