FLOW：用于广义远程生理测量的特征级最优扭曲

中文摘要 AI 辅助

远程光电容积描记（rPPG）实现了非接触式生理测量，但仍易受光照、运动和传感器引起的域偏移影响。我们提出了FLOW（特征级最优扭曲），一个基于最优传输的框架，用于域泛化的rPPG。FLOW集成了时间细化模块（TRM）以稳定时间动态，以及基于原型的跨时间最优传输（PCOT）模块，通过可学习的特征对齐实现域不变对齐。在特征对齐方面，FLOW采用软跨时间对应建模，以灵活的方式对齐时间特征，使模型能够尊重并保留生理信号的固有节律模式。此外，我们模块的轻量级设计使其能够无缝集成到现有的端到端rPPG架构中，无需额外的预处理。两个正则化项进一步强制源一致性和身份保持。理论上，我们在条件最优传输下推导了泛化界。在四个rPPG基准上的大量实验表明，FLOW以轻量级设计和强生理保真度实现了最先进的跨域性能。

英文摘要

Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains vulnerable to domain shifts from illumination, motion, and sensors. We propose \textbf{FLOW (Feature-Level Optimal Warping)}, an \emph{optimal transport--driven} framework for domain-generalized rPPG. FLOW integrates a \textbf{Temporal Refinement Module (TRM)} to stabilize temporal dynamics and a \textbf{Prototype-based Cross-Temporal Optimal Transport (PCOT)} module to achieve domain-invariant alignment via learnable prototypes.Beyond feature alignment, FLOW employs soft cross-temporal correspondence modeling that aligns temporal features in a flexible manner, allowing the model to respect and preserve the intrinsic rhythmic patterns of physiological signals. Moreover, the lightweight design of our modules allows seamless integration into existing end-to-end rPPG architectures without additional preprocessing. Two regularization terms further enforce source consistency and identity preservation. Theoretically, we derive a generalization bound under conditional optimal transport. Extensive experiments across four rPPG benchmarks show that FLOW achieves state-of-the-art cross-domain performance with lightweight design and strong physiological fidelity.

发表机构

• Great Bay University（大湾区大学）
• Hefei University of Technology（合肥工业大学）
• Southern University of Science and Technology（南方科技大学）
• Macao Polytechnic University（澳门理工大学）
• Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
• Dongguan Key Laboratory for Intelligence and Information Technology（东莞市智能与信息技术重点实验室）

机构由 AI 辅助整理，请以论文原文为准。