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

2026-02-11

AI Summary

This study investigates the sociocultural biases embedded in artificial intelligence systems during the generation and constraint of human movement. Integrating human performers with quadrupedal robots, the project employs large language models to generate contrasting movement instructions derived from science fiction texts, which are then translated via diffusion video models into choreographic guidance and motor control signals. These movements are synchronously performed within a mirrored space and captured from multiple viewpoints to reconstruct a 3D digital archive of motion trajectories. Innovatively adopting an archaeological lens, the work traces movement artifacts to retroactively uncover cultural encodings within AI, fostering an immersive dialogue among humans, machines, and generative systems. The resulting archive constitutes the first traceable, AI-driven human-robot co-performance record, offering a tangible revelation of algorithmic bias and prompting profound reflection on what it means to be human in the era of embodied AI.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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?

Problem

• artificial intelligence
• bodily movement
• generative systems
• socio-cultural biases
• human-machine interaction

Innovation

• multi-agent embodied performance
• generative AI choreography
• LLM-guided motion generation
• human-robot interaction
• digital motion archaeology