HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation

Ba-Thinh Nguyen; Thi-Duyen Ngo; Thanh-Trung Huynh; Thanh-Ha Le; Huy-Hieu Pham

cs.CVApr 2, 2026

HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation

Authors: Ba-Thinh Nguyen, Thi-Duyen Ngo, Thanh-Trung Huynh, Thanh-Ha Le, Huy-Hieu Pham

Organizations: VNU University of Engineering and Technology, Hanoi, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam · Center for Innovations in Health Sciences, VinUniversity, Hanoi, Vietnam

Abstract

Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos; however, its practical deployment is often hindered by substantial performance degradation under domain shift. While recent deep learning-based rPPG methods have achieved strong performance on individual datasets, they frequently overfit to appearance-related factors, such as illumination, camera characteristics, and color response, that vary significantly across domains. To address this limitation, we introduce frequency domain adaptation (FDA) as a principled strategy for modeling appearance variation in rPPG. By transferring low-frequency spectral components that encode domain-dependent appearance characteristics, FDA encourages rPPG models to learn invariance to appearance variations while retaining cardiac-induced signals. To further support physiologically consistent alignment under such appearance variation, we propose Harmonic-Constrained Optimal Transport (HOT), which leverages the harmonic property of cardiac signals to guide alignment between original and FDA-transferred representations. Extensive cross-dataset experiments demonstrate that the proposed FDA and HOT framework effectively enhances the robustness and generalization of rPPG models across diverse datasets.

Figures & tables

Figure 1: Comparison with existing rPPG methods. Conventional approaches are trained on labeled source-domain data and directly deployed to the target domain, resulting in performance degradation under domain shift. In contrast, ours leverages unlabeled target-domain samples to synthesize source data with target-domain appearance during training, encouraging appearance-invariant learning and improving cross-domain generalization.

Figure 3: Illustration of FDA: low-frequency amplitude replacement in the Fourier domain while preserving the phase spectrum.

Model	Venue	PURE $\rightarrow$ UBFC-rPPG				UBFC-rPPG $\rightarrow$ PURE				PURE $\rightarrow$ MMPD				UBFC-rPPG $\rightarrow$ MMPD
Model	Venue	MAE $\downarrow$	MAPE $\downarrow$	RMSE $\downarrow$	$r\uparrow$	MAE $\downarrow$	MAPE $\downarrow$	RMSE $\downarrow$	$r\uparrow$	MAE $\downarrow$	MAPE $\downarrow$	RMSE $\downarrow$	$r\uparrow$	MAE $\downarrow$	MAPE $\downarrow$	RMSE $\downarrow$	$r\uparrow$
DeepPhys [ 2 ]	ECCV’18	1.46	1.65	3.25	0.98	7.63	7.35	21.97	0.67	22.09	24.38	30.38	0.04	24.50	27.29	31.79	0.01
+ HOT	–	1.31	1.48	3.04	0.99	7.46	7.48	21.95	0.67	19.73	20.07	26.46	0.08	20.65	22.84	24.97	0.05
PhysNet [ 29 ]	BMVC’19	1.70	1.23	3.63	0.99	7.87	15.89	18.29	0.81	16.06	18.46	23.59	0.10	11.25	18.00	23.59	0.34
+ HOT	–	1.17	1.26	3.10	0.99	5.38	12.61	16.52	0.87	12.87	15.90	19.79	0.16	10.36	14.08	18.42	0.38
TSCAN [ 13 ]	NeurIPS’20	1.32	1.49	3.01	0.98	5.98	6.65	19.68	0.73	18.55	20.20	27.13	0.06	18.13	19.92	26.78	0.05

Table 1: Cross-dataset test results. PURE

\rightarrow

UBFC-rPPG means the model is trained on PURE and tested on UBFC-rPPG. Lower is better for MAE/MAPE/RMSE, while higher is better for Pearson correlation

r

Figure 4: Cross-dataset evaluation on UBFC-rPPG $\rightarrow$ MMPD. From left to right: Bland–Altman plot of PhysNet + HOT, Bland–Altman plot of PhysNet, scatter plot of PhysNet + HOT, and scatter plot of PhysNet. The dashed horizontal lines in the Bland–Altman plots denote the 95% limits of agreement, while the dashed diagonal lines in the scatter plots denote the identity line.

Figure 5: Effect of the low-frequency replacement ratio $\beta$ on cross-dataset performance.

Figure 6: Visual examples of low-frequency replacement under different ratios $\beta$ .

Figure 7: Performance comparison on PhysNet under progressive integration of the geometric OT loss and the harmonic-constrained OT loss into the baseline.

Figure 8: Heatmap of MAE across different $\lambda_{h}$ values and Sinkhorn iterations. Each cell corresponds to the MAE obtained under a specific hyperparameter combination. Lighter cells indicate lower error (better performance), and the red marker denotes the optimal setting.

Method	Train Time $\downarrow$	Peak Memory $\downarrow$	Latency $\downarrow$	Throughput $\uparrow$
	(ms/iter)	(GB)	(ms)	(Kfps)
PhysNet w/o HOT	391.3	1.27	1.92	66.75
PhysNet w/ HOT	434.8	2.52	1.89	67.82
Relative Change	+11.1%	+98.4%	-1.6%	+1.6%

Table 2: Computational overhead of HOT on PhysNet [ 29 ] . Training efficiency is measured by per-iteration training time and peak GPU memory. Inference efficiency is measured by forward-only latency and throughput. Lower is better for Train Time, Peak Memory, and Latency; higher is better for Throughput.

Remote Photoplethysmography

Remote photoplethysmography (rPPG) estimates pulse and other physiological signals from video without contact sensors.

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FLOW: Feature-Level Optimal Warping for Generalized Remote Physiological Measurement

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Great Bay University · Harbin Institute of Technology, Shenzhen · Hefei University of Technology+3

Remote Photoplethysmography Domain Generalization Optimal Transport

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Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains vulnerable to domain shifts from illumination, motion, and sensors. We propose FLOW (Feature-Level Optimal Warping), an optimal transport--driven framework for domain-generalized rPPG. FLOW integrates a Temporal Refinement Module (TRM) to stabilize temporal dynamics and a 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.

Abstract

Remote Photoplethysmography

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