生成可换装的虚拟人体步态数据集，解决真实场景中服装变化带来的识别难题
BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition
- 将真人映射至虚拟引擎，实现服装自由变换同时保持步态身份一致
- 在自建数据集BarbieGait上，跨服装识别准确率显著提升
- 适合关注步态识别鲁棒性与合成数据应用的研究者
步态识别作为可靠的生物特征技术近年来发展迅速，但现实世界中多样的服装样式带来了严峻挑战。本文提出BarbieGait，一个合成步态数据集，将真实受试者独特映射至虚拟引擎，模拟大量服装变化的同时保留其步态身份信息。作为开创性工作，BarbieGait提供了可控的步态数据生成方法，可生成大规模数据以验证跨服装识别难题。然而，服装多样性增加了类内差异，成为学习服装不变特征的主要挑战。为此，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。通过大量实验验证，该方法在BarbieGait及现有主流步态基准上均显著提升了跨服装识别性能。我们认为，具备丰富跨服装步态数据的BarbieGait将推动步态识别在跨服装场景下的能力提升，并促进相关研究进展。
原文摘要 · Abstract (English)
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.
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