ReTracing: An Archaeological Approach Through Body, Machine, and Generative Systems

Yitong Wang, Yue Yao

Track: Artwork

Keywords: generative AI, human–machine interaction, embodied performance, bias in AI, AI art, robotic art

TL;DR: ReTracing adopts an archaeological approach to examine how artificial intelligence shapes, constrains, and produces bodily movement through human–machine interactions.

Abstract: We present ReTracing, a multi-agent embodied performance art that adopts an archaeological approach to examine how artificial intelligence shapes, constrains, and produces bodily movement. Drawing from science-fiction novels, the project extracts sentences that describe human–machine interaction. We use large language models (LLMs) to generate paired prompts—“what to do” and “what not to do”—for each excerpt. A diffusion-based text-to-video model transforms these prompts into choreographic guides for a human performer and motor commands for a quadruped robot. Both agents enact the actions on a mirrored floor, captured by multi-camera motion tracking and reconstructed into 3D point clouds and motion trails, forming a digital archive of motion traces. Through this process, ReTracing serves as a novel approach to real how generative systems encode socio-cultural biases through choreographed movements. Through an immersive interplay of AI, human, and robot, ReTracing confronts a critical question of our time: What does it mean to be human among AIs that also move, think, and leave traces behind?

Submission Number: 159