BarbieGait：面向步态识别的身份一致且支持多样换装的合成人体数据集
BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition
用虚拟引擎把真实受试者映射为可控换装的合成步态数据，为跨衣物步态识别提供可扩展训练/评测源；GaitCLIF 的衣物不变特征学习思路可迁移到其他需抑制外观变化的生物特征识别任务。
原文依据 real-world subjects are uniquely mapped into a virtual engine to simulate extensive clothing changes while preserving their gait identity information
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论步态识别作为一种可靠的生物特征技术，近年来发展迅速，但同时面临真实世界中多样服装风格带来的重大挑战。本文提出 BarbieGait，一个合成步态数据集，将真实世界受试者唯一地映射到虚拟引擎中，在保留其步态身份信息的同时模拟大量服装变化。作为一项开创性工作，BarbieGait 提供了一种可控的步态数据生成方法，能够生成大规模数据集，以验证真实数据难以验证的跨服装问题。然而，服装的多样性增大了类内方差，使得在变化服装条件下学习服装不变特征成为最大挑战之一。因此，我们提出 GaitCLIF（面向步态的服装不变特征）作为跨服装步态识别的稳健基线模型。通过大量实验，我们验证了该方法在 BarbieGait 以及现有主流步态基准上显著提升了跨服装性能。我们相信，BarbieGait 凭借其丰富的跨服装步态数据，将进一步推动跨服装场景下步态识别能力的发展，并促进相关研究的进步。源代码与数据集见 https://github.com/BarbieGait/BarbieGait。
英文原文摘要 1382 字符
Gait recognition, as a reliable biometric technology, has seen rapid development in recent years while it faces significant challenges caused by diverse clothing styles in the real world. This paper introduces BarbieGait, a synthetic gait dataset where real-world subjects are uniquely mapped into a virtual engine to simulate extensive clothing changes while preserving their gait identity information. As a pioneering work, BarbieGait provides a controllable gait data generation method, enabling the production of large datasets to validate cross-clothing issues that are difficult to verify with real-world data. However, the diversity of clothing increases intra-class variance and makes one of the biggest challenges to learning cloth-invariant features under varying clothing conditions. Therefore, we propose GaitCLIF (Gait-oriented CLoth-Invariant Feature) as a robust baseline model for cross-clothing gait recognition. Through extensive experiments, we validate that our method significantly improves cross-clothing performance on BarbieGait and the existing popular gait benchmarks. We believe that BarbieGait, with its extensive cross-clothing gait data, will further advance the capabilities of gait recognition in cross-clothing scenarios and promote progress in related research. The source code and dataset are available at https://github.com/BarbieGait/BarbieGait.