用AI生成舞步，让人类与机器人共舞，追溯技术如何塑造身体动作。
ReTracing: An Archaeological Approach Through Body, Machine, and Generative Systems
- 从科幻小说提取人机互动语句，用大模型生成动作指令。
- 将指令转为视频与机器人控制信号，双端同步表演并记录轨迹。
- 通过运动痕迹揭示生成模型隐含的社会文化偏见，适合关注AI艺术者。
我们提出ReTracing，一种多智能体具身表演艺术，采用考古学视角探究人工智能如何塑造、限制并生成身体运动。项目从科幻小说中提取描述人机交互的句子，利用大语言模型（LLMs）生成对应的动作指令（“该做什么”和“不该做什么”）。基于扩散模型的文本到视频系统将这些指令转化为人类表演者的编舞指南及四足机器人的运动指令。两人类与机器人在镜面地板上同步执行动作，通过多相机运动捕捉系统记录，并重建为3D点云与运动轨迹，形成数字运动档案。这一过程揭示了生成系统如何通过编排动作编码社会文化偏见。在人工智能亦能移动、思考并留下痕迹的背景下，ReTracing提出了一个时代核心问题：当AI也行动、思考并留下痕迹时，何为人类？
原文摘要 · Abstract (English)
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?
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