姿态估计是否已准备好应对开放世界？STAGE：一种用于审计3D人体姿态估计器的生成人工智能工具包
Are Pose Estimators Ready for the Open World? STAGE: A GenAI Toolkit for Auditing 3D Human Pose Estimators
AI总结：
STAGE通过生成人工智能工具包评估3D人体姿态估计器的鲁棒性，揭示其在开放世界部署中的敏感性问题。
摘要：
在安全关键应用中，在部署前对3D人体姿态估计器进行审计至关重要。如果天气或服装发生变化，系统是否会崩溃？它是否在性别和年龄方面具有鲁棒性？为了回答这些问题，我们需要具有单个属性差异的受控研究，但现实基准无法提供这样的配对。因此，我们提出了STAGE，一种用于审计3D人体姿态估计器的生成人工智能数据工具包。对于STAGE，我们开发了第一个具有准确3D姿态控制的生成人工智能图像创建器，并提出了一种新的评估策略，以隔离和量化诸如性别、种族、年龄、服装、地点和天气等单因素的影响。借助STAGE，我们生成了一系列基准，首次审计了流行姿态估计器对这些因素的敏感性。我们的结果表明，自然变化可能严重降低姿态估计器的性能，对它们在开放世界部署中的准备性提出质疑。我们旨在突出这些鲁棒性问题，并建立STAGE作为基准来量化这些问题。
英文摘要：
For safety-critical applications, it is crucial to audit 3D human pose estimators before deployment. Will the system break down if the weather or the clothing changes? Is it robust regarding gender and age? To answer these questions and more, we need controlled studies with images that differ in a single attribute, but real benchmarks cannot provide such pairs. We thus present STAGE, a GenAI data toolkit for auditing 3D human pose estimators. For STAGE, we develop the first GenAI image creator with accurate 3D pose control and propose a novel evaluation strategy to isolate and quantify the effects of single factors such as gender, ethnicity, age, clothing, location, and weather. Enabled by STAGE, we generate a series of benchmarks to audit, for the first time, the sensitivity of popular pose estimators towards such factors. Our results show that natural variations can severely degrade pose estimator performance, raising doubts about their readiness for open-world deployment. We aim to highlight these robustness issues and establish STAGE as a benchmark to quantify them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
