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

TL;DR

ReTracing is an innovative multi-agent performance art project that uses AI-generated choreography and robotics to explore how artificial intelligence influences human and machine movement, revealing socio-cultural biases.

Contribution

It introduces a novel archaeological approach combining AI, robotics, and performance to analyze socio-cultural biases in generative systems through embodied movement.

Findings

Generated digital archive of motion traces from human and robot performances.

Revealed socio-cultural biases encoded in AI-generated movements.

Established a new method for analyzing AI influence on embodied behavior.

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 reveal 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?