Remote photoplethysmography (rPPG) estimates a blood volume pulse (BVP) waveform from facial videos captured by commodity cameras. Although recent deep models improve robustness compared to classical signal-processing approaches, many methods increase computational cost and parameter count, and attention-based temporal modeling introduces quadratic scaling with respect to the temporal length. This paper proposes ToTMNet, a lightweight rPPG architecture that replaces temporal attention with an FFT-accelerated Toeplitz temporal mixing layer. The Toeplitz operator provides full-sequence temporal receptive field using a linear number of parameters in the clip length and can be applied in near-linear time using circulant embedding and FFT-based convolution. ToTMNet integrates the global Toeplitz temporal operator into a compact gated temporal mixer that combines a local depthwise temporal convolution branch with gated global Toeplitz mixing, enabling efficient long-range temporal filtering while only having 63k parameters. Experiments on two datasets, UBFC-rPPG (real videos) and SCAMPS (synthetic videos), show that ToTMNet achieves strong heart-rate estimation accuracy with a compact design. On UBFC-rPPG intra-dataset evaluation, ToTMNet reaches 1.055 bpm MAE with Pearson correlation 0.996. In a synthetic-to-real setting (SCAMPS to UBFC-rPPG), ToTMNet reaches 1.582 bpm MAE with Pearson correlation 0.994. Ablation results confirm that the gating mechanism is important for effectively using global Toeplitz mixing, especially under domain shift. The main limitation of this preprint study is the use of only two datasets; nevertheless, the results indicate that Toeplitz-structured temporal mixing is a practical and efficient alternative to attention for rPPG.

翻译：远程光电容积描记（rPPG）技术通过消费级摄像头采集的面部视频来估计血容量脉冲（BVP）波形。尽管近期基于深度学习的方法相比传统信号处理方法提升了鲁棒性，但许多方法增加了计算成本和参数量，且基于注意力的时序建模会带来与时间长度相关的二次方复杂度。本文提出ToTMNet，一种轻量化的rPPG架构，使用时序注意力机制替换为FFT加速的Toeplitz时序混合层。Toeplitz算子利用与视频片段长度呈线性关系的参数量即可实现全序列时序感受野，并可通过循环嵌入和基于FFT的卷积以近线性时间复杂度进行计算。ToTMNet将全局Toeplitz时序算子集成至紧凑的门控时序混合器中，该混合器结合了局部深度时序卷积分支与门控全局Toeplitz混合，在仅含6.3万参数的情况下实现了高效的长程时序滤波。在UBFC-rPPG（真实视频）和SCAMPS（合成视频）两个数据集上的实验表明，ToTMNet以紧凑的设计实现了优异的心率估计精度。在UBFC-rPPG数据集内评估中，ToTMNet达到1.055 bpm平均绝对误差与0.996皮尔逊相关系数。在合成到真实场景迁移（SCAMPS至UBFC-rPPG）中，ToTMNet达到1.582 bpm平均绝对误差与0.994皮尔逊相关系数。消融实验证实门控机制对于有效利用全局Toeplitz混合至关重要，尤其在领域偏移情况下。本预印本研究的主要局限在于仅使用两个数据集；尽管如此，结果表明Toeplitz结构时序混合是rPPG中替代注意力机制的一种实用且高效的方案。