In this paper, we propose Periodic-MAE, a self-supervised framework for learning generalizable spatio-temporal representations of periodic physiological signals from unlabeled facial videos. The proposed method leverages a masked autoencoder (MAE), which learns high-dimensional facial representations by reconstructing masked video tokens without relying on remote photoplethysmography (rPPG) specific supervision. To explicitly align representation learning with the characteristics of rPPG, we introduce a periodicity-aware frame masking strategy based on video resampling, enabling the encoder to learn representations that capture quasi-periodic temporal patterns relevant to pulse signal estimation. In addition, physiological bandlimit constraints are integrated into the MAE pre-training framework, exploiting the sparsity of pulse signals in the frequency domain to guide the learned representations toward physiologically meaningful patterns. After pre-training, the learned representations are transferred to downstream rPPG estimation, where the encoder serves as a generic feature extractor for recovering pulse-related signals from facial videos. We conduct extensive experiments on four benchmark datasets, including PURE, UBFC-rPPG, MMPD, and V4V. Moreover, we evaluate the proposed approach on a real-world rPPG dataset collected under unconstrained lighting conditions and subject motion. Experimental results demonstrate that Periodic-MAE consistently improves rPPG estimation performance, particularly in challenging cross-dataset and real-world evaluation settings. Our code is available at https://github.com/ziiho08/Periodic-MAE.

翻译：本文提出Periodic-MAE，一种从无标注人脸视频中学习周期性生理信号可泛化时空表征的自监督框架。该方法利用掩码自编码器（MAE），通过重构被掩码的视频令牌来学习高维人脸表征，无需依赖远程光电容积描记法（rPPG）的特定监督。为显式地将表征学习与rPPG特性对齐，我们引入一种基于视频重采样的周期感知帧掩码策略，使编码器能学习捕获与脉搏信号估计相关的准周期时间模式的表征。此外，生理频带限制约束被整合到MAE预训练框架中，利用脉冲信号在频域的稀疏性引导所学习的表征朝向具有生理意义的模式。预训练后，学习到的表征通过迁移学习应用于下游rPPG估计任务，此时编码器作为通用特征提取器从人脸视频中恢复脉搏相关信号。我们在四个基准数据集（包括PURE、UBFC-rPPG、MMPD和V4V）上进行了广泛实验。同时，我们在无约束光照条件和受试者运动条件下采集的真实场景rPPG数据集上评估了所提方法。实验结果表明，Periodic-MAE能持续提升rPPG估计性能，尤其在具有挑战性的跨数据集和真实场景评估设置中表现优异。我们的代码已开源至https://github.com/ziiho08/Periodic-MAE。