PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing
Authors: Yiping Xie
Bo Zhao
Mingtong Dai
Jian-Ping Zhou
Yue Sun
Tao Tan
Weicheng Xie
Linlin Shen
Zitong YU
Venue: ICLR 2026 Poster (2026)
Keywords: Remote photoplethysmograph
large language model
heart rate
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: PhysLLM is a remote photoplethysmography framework that combines large language models with domain-specific components, improving physiological signal measurement accuracy and robustness.

Abstract: Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling. Large Language Models (LLMs) excel at capturing long-range dependencies, offering a potential solution but struggle with the continuous, noise-sensitive nature of rPPG signals due to their text-centric design. To bridge this gap, we introduce PhysLLM, a collaborative optimization framework that synergizes LLMs with domain-specific rPPG components. Specifically, the Text Prototype Guidance (TPG) strategy is proposed to establish cross-modal alignment by projecting hemodynamic features into LLM-interpretable semantic space, effectively bridging the representational gap between physiological signals and linguistic tokens. Besides, a novel Dual-Domain Stationary (DDS) Algorithm is proposed for resolving signal instability through adaptive time-frequency domain feature re-weighting. Finally, rPPG task-specific cues systematically inject physiological priors through physiological statistics, environmental contextual answering, and task description, leveraging cross-modal learning to integrate both visual and textual information, enabling dynamic adaptation to challenging scenarios like variable illumination and subject movements. Evaluation on four benchmark datasets, PhysLLM achieves state-of-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.

Published as a conference paper at ICLR 2026
PHYsLLM: HARNESSING LARGE LANGUAGE MODELS FOR CROSS-MODAL REMOTE PHYSIOLOGICAL SENSING
Yiping Xiel:2, Bo Zhao² Mingtong Dai², Jian-Ping Zhou Yue Sun', Tao Tan',
Weicheng Xie Linlin Shen 1.3 & Zitong 2.34
'Shenzhen University Great Bay University
National Engineering Laboratory for Big Data System Computing Technology
A 'Dongguan Key Laboratory for Intelligence and Information Technology
Guangdong Medical University SSutherr Medical University (Dongguan People's Hospital)
7 Maaao Polytechnic University
2310275054@email.
mt www.:aaai...u.u....mmm..mm edu. en, 225040248@link. cuhk edu. cn
choujianping0165@smma
yuesun@mpu.edu.mm taotanjs@gmal com com
mexieeszu.edu.co
yulige.u@leee... org
ABSTRACT
Remote photoplethysmography (rPPG) enables non-contact physiological surement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling. Large Language Models (LLMs) excel at captur- ing long-range dependencies, offering potential solution but struggle with the continuous, moise-sensitiv nature of rPPG signals due their text-centric design. To bridge this gap, we introduce the PhysLLM, collaborative optimization frame- work that synergizes LLMs with domain-specific rPPG components. Specifically, the Text Prototype Guidance (TPG) strategy is proposed to establish cross- modal alignment by projecting hemodynamic features into LL.M-inteprretable semantic space, effectively bridging the representational gap between physiological signals Stationary (DDS) Algorithm
and linguistic tokens. Besides, novel is proposed for resolving signal instability through adaptive time-frequency do- main feature re- re-weighting. Finally, rPPG task-specific cues systematically inject physiological priors through physiological statistics, environmental contextual an- swering, and task description, leveraging cross-modal learning to integrate both visual and textual information, enabling dynamic adaptation to challenging sce- narios like variable illumination and subject movements. Evaluation on four benchmark datasets, PhysLLM achieves tfat-oo-tthe-ar accuracy and robustness. demonstrating superior generalization across lighting variations and motion sce- narios.
INTRODUCTION
Remote photoplethysmography (rPPG) is a non-contact technique that allows for the remote mea- surement of physiological signals such as heart rate (HR) (Yue et al., 2021: Das et al. 2023) and blood pressure (Wu et al. 2022) by analyzing subtle color changes in the skin caused by blood flow. Unlike traditional contact-based methods, such eleetrocardiograms (ECG) and Photoplethysmo- graph (PPG) (Murthy et al. 2015), rPPG does not require physical sensors attached to the body, making it
convenient and less intrusive option for continuous health monitoring. Based on these advantages of rPPG, more and more excellent work and research have been committed to improving its accuracy, robustness and scalability in recent years.
Traditional remote photoplethysmograplyy (rPPG) methods (Poh et al. 2010 Madej et al., 2011
2013) Wang al.] (017) typically rely signal processing techniques to isolate rPPG signals from videos. More recent deep learning methods, including CNNs (Yu et al.
De Haan & Jeanne
2019
"Equal contribution
1 CCorespondingg authors.

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Published as a conference paper at ICLR 2026
CNN-baser
(c). PhysLLM
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Qr IPC shoue donnaly arroes Affover
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Figure 1: The comparison between (a) typical ENN-based rPPG model, (b) pure LLM model, and (c) proposed LhysLLM.
Li et al. 2023 Lu et al. 2023; Niu et al., 2019 Spetlik et al. 2018 Chen & McDuff 2018 Liu
al.] 2b221b and Transformers Yu et al. 2022 Shao al. 2023 Qian al., 2024] Yu al. 2023),
have been proposed to address some of these challenges by learning more complex representations of facial features. Fig. a shows a typical CNN-based framework where features extracted by the encoder are directly fed into the estimator to produce the final sequence. However, these methods are still sensitive visual noise (e.g.. motion blur, occlusions, and low resolution) and often rely on single video stream, which limits robustness in real-world settings. Introducing textual descriptions provides complementary context about scene conditions-suce as ocelusion, motion artifacts, and lighting changes- so the model can adapt its processing accordingly. This multi-mode design lets the L.LM combine visual cues with semantic context, improving physiological signal extraction under challenging conditions.
Large Language Models (LLMs) offer significant advantages for enhancing rPPG methods, partic- ularly in modeling long-term temporal dependencies (Tang et al. 2025 Jin et al. 2023). While traditional rPPG approaches struggle with extended video sequences, LLMs excel at capturing com- plex sequential patterns, making them promising candidates for physiological signal estimation. Previous works like TimeI.LM (Jin et al. 2023) have demonstrated the effectiveness of L.MSs in time-series applications, suggesting untapped potential for rPPG tasks. However, directly applying LLMs to rPPG estimation presents fundamental challenges, as illustrated in Fig. (bb) The mismatch between LLMs' discrete operations and rPPG features' continuous nature leads to poor represen- tations and high noise sensitivity. Despite LLMs' proven capabilities in cross-modal tasks, their text-oriented architecture requires significant adaptation for effective physiological signal analysis.
To address these limitations, we propose PhysLLM, a collaborative optimization framework that integrates LLMs with specialized rPPG processing components. As shown in Fig. [Tc), to combine CNN's local spatio-temporal feature extraction with LLM's s superior long-range temporal reasoning for more robust physiological measurements, we employ a CNN-based rPPG as the base model, leveraging both the multi-scale features and the estimated rPPG signals to guide LLM learning. Specifically, we introduce a Dual-Domain Stationary (DDS) Algorithm to stabilize the base model's rPPG output and Vision Aggregator module fuse multi-scale hemodynamic features. To bridge the gap between rPPG features and LLM processing, we propose Text Prototype Guidance (TPG), which aligns sequence and multi-scale features with LLM text prototypes. Furthermore, we introduce task-specific cues, i.e., environmental factors, physiological knowledge, and task description, with learnable word vectors to enhance the LLM's understanding the rPPG context conuux cross-modal learning, integrating both visual and textual information. Therefore, PhysLLM adapts to varying conditions such as lighting changes and motion artifacts, signilicantly improving the accuracy and robustness of rPPG measurement. Overall, our contributions include:
We propose PhysLLM, the first framework integrating LLMs into rPPG measurement to establish interpretable connections between physiological dynamics and contextual seman- tics. The architecture's 's inherent capacity for long-sequence dependency modeling enables superior performance in complex real-life scenarios.
We propose a novel time time-frequency Dual-Domain stationary (DDS) Algorithm to address spectral-temporal instability through adaptive coefficient modulation with exponential decay characteristics. DDS ensures the processed time series to maintain periodic consistency while reducing noise interference.
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Aduptive Prompt Learning (APL))
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Figure 2: Framework the PhysLLM. The architecture PhysLLM comprises three principal data streams that operate in concert. The leftmost stream represents the Physiological Cue-Aware Prompt Learning module, which incorporates task-specific prior knowledge through adaptive prompt learning while generating context- aware prompt tokens. The central and rightmost streams collectively form the Text-Vision-Sequee Generation pipeline, which integrates
Embedding novel Dual-Domain Stationary (DDS) Algorithm and Text Prototype Guidance (TPG) module. This integrated approach facilitates the extraction of sequence tokens from temporal physiological data and visual tokens from facial imagery, both guided by LLM-generated text prototypes that serve semantic anchors for cross-modal alignment.
We design task-specific cues to inject physiological priors through physiological statistics, environmental context and task description, enabling dynamic adaptation to challenging scenarios.
We propose the Text Prototype Guidance (TPG) strategy establish cross-modal alignment by projecting hemodynamic features into LLLM-inteppretable semantic space, significantly reducing the gap between sequential, visual, and textual modalities.
Extensive experiments demonstrate the superiority of PhysLLM even under scenarios with serious degradation.
2 RELATED WORK
Remote Physiological Measurement. rPPG has advanced significantly from early signal pro- cessing methods to deep learning-based approaches. Traditional techniques, such as blind source separation and skin reflection- based models (Poh et al. 2010 Madej et al.) 2011 De Haan & Jeanne, 2013 Wang al. 2017). , initially aimed to isolate phystological signals but struggled with real world variability. Later methods incorporated physiological priors to improve robustness (Zhang et al. 2024) yet they remained sensitive noise and illumination. With the rise of deep learning, more recent approaches to rPPG measurement have leveraged the power of neural networks to capture rich spatio-tempoltt
representations. Early deep leerninnnn based methods introduced end end-to-end spa- tiotemporal networks (Spetlik et al. 2018 Liu al. 2020 Yu et al. 2019 Chen & McDuff 2018 Niu et al. 2019 Liu et al. 20221b) Yu et al. [2023] 2022). These models demonstrated superior per- formance compared to traditional methods, as they were capable of capturing intricate temporal and spatial relationships within the video data. One major limitation is their sensitivity to visual interfer- ence, such as motion blur, occlusions, and varying lighting conditions, all of which can compromise the quality of the extracted rPPG signal. Recent advancements in vision-language multi-modal learn- ing ( (Yue al. 2024) have shown great promise enhancing robustness by aligning with additional modalities for PPG measurement.
LLMs for Time Series Tasks. Recent advancements in LLMs have demonstrated remarkable potential in time series analysis tasks, particularly through their ability to learn temporal patterns and perform cross-modal reasoning. Several pioneering works have explored reprogramming LLMs for time series forecasting without architectural modifications. For instance, Lime-LLM (Jin et al. 2023) introduces a novel reprogramming framework that aligns time series embeddings with LLM token spaces using learnable prompt tokens, achieving state-of-the-art performance across multi-
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ple domains. This approach builds upon foundational insights from Time Series Forecasting with LLMs (Gruver et al. 2023), which systematically evaluates LLMs' inherent temporal reasoning capabilities and proposes specialized fine-tuning strategies to enhance their forecasting accuracy. In healthcare applications, L.Ms have shown particular promise for physiological time series inter- pretation (Liu et al., 2023b). While existing research primarily focuses standard physiological signals like ECG and EEG, the application of LLMs to rPPG analysis remains underexplored. Cur- rent approaches for rPPG-based health monitoring typically rely on specialized neural architectures, potentially overlooking the cross-modal generalization capabilities inherent in foundation models. Our work bridges this gap by adapting the time series reprogramming module while incorporating rPPG-aware vision-language knowledge, enabling LLMs to interpret subtle cardiovascular patterns from facial videos.
3 METHODOLOGY
The overall architecture of the proposed P is designed to integrate textual, visual, and signal information into a unified framework, enabling robust rPPG physiological estimation. As illustrated in Fig. 2 the framework consists of two key components: Text-Vision-Sequee ating (the right part of Fig. 2 and Physiological Cue-Aware Prompt Learning (the left part of Fig. 2 The detailed components of the model and the training objectives are described below.
Embedding Gener-
3.1
EMBEDDING GENERATING
While current deep learning frameworks struggle to extract reliable rPPG signals from facial videos due to environmental interference, existing physiological priors in models like PhysNet (Yu et al.) 2019) demonstrate inherent robustness to such variations. Building on this foundation, we establish novel architecture with: 1) A fixed PhysNet (Yu et al. (019) backbone preserving rPPG signals and spatio-temporal features, 2) Three trainable enhancement modules (Dual-Domaii Stationary Algorithm module for stable signal tokenization, dedicated multi-scale interaction module for hierar- chical feature extraction, Text Prototype Guidance module aligning these representations with LLM semantic spaces) that progressively refine the physiological representations.
Doal-Domain Stationary (DDS) Algorithm. To effectively reduce the interference of noise on the model and further enhance enhance its robustness and prediction accuracy, we propose novel algorithm. This algorithm optimizes the signal processing workflow in a targeted manner, significantly improving the quality of rPPG signals and providing more reliable data foundation for subsequent analysis.
Specifically, let x E RxL denote the raw rPPG waveform extracted by the PhysNet backbone YYu at. 2019), where batch size and sequence length inherit the output dimensions of the backbone. We first compute the global mean and global standard deviation or across the entire sequence,
The normalized output x' is then calculated as:
1=
We denote similar standardization processes in a unified manner as function H(-). To ensure global stationarity, we proceed temporal smoothing with the following operations:
time= = .1, (1 -1-oo a)) 21-11 =
time
where i denotes the current frame value, and a is a smoothing factor. The proof of stationarity is provided in Appendix. C In the following text, we denote these smoothing operations collectively as (-). Therefore, the above operation can be simplified as ziime Z(x'.
In parallel, the module performs frequency domain decomposition using the discrete wavelet trans- form (DWT), The DWT decomposes the input signal into approximation coefficients (ac) and detail coefficients (de) on multiple scales. Specifically, the results can be expressed as:
Xac- [Adde.1.... ..ee..,..........
x ] DWT(x),
where / is the decomposition level (J = 3 in this implementation). After normalization, the inverse wavelet transform (IDWT) reconstructs the smoothed frequency-domaii representation z.re:
fre IDWT
|Z (H((xx,,))) Z ()(x).
(4)
To combine the advantages of time-domain and frequency-domain processing, an adaptive weighting mechanism is introduced. The final smoothed output Xrec is computed as:
z (1 B) zie B f
(5)
where / [0, 1] is learnable parameter.
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Multi-scale Interaction via Vision Aggregator (VA). We introduce the multi-scale Interaction module to effectively integrate multi-scale features from different modalities. As shown in Fig. 3 this module leverages combination of cross-attention and self-attention mechanisms to capture both inter-modal and intra-modal relationships between high-level and low-level features. By employing learnable scaling parameters, the module ensures that the fused feature representation is adaptive and context-aware.
Specifically, we extract M layers fea- tures from the backbone, denoted as T [f,, for fa..... J.]. , in which fi e @RxTxMxWW To align these features into common embedding space, we employ a set of feature projection lay- ers, F Projection(fi. harger), where harger is the setting length after com- pression.
Self
Afrrriss
Cont Contaatio sttonn
Coos Allarion
Figure 3: Architecture of the Vision Aggregator, It employs
Since the strong semantic alignment between deep visual features and text space, use the deep feature FM queries to dynamically extract missing details from shallow features Concat Fi, F2....,
hierarchical attention architecture to dynamically synthesize multi-scale feature representations.
This results in visual feature Feross E RBxTxD with richer fine-graines features. This process be formulated as:
Foross CrossnAttntoon M X), where CrossAAttention()) denotes the cross-attention mechanism, Concat(-) represents the concatena- tion operation.
(6)
To enhance the representation by capturing internal dependencies within the cross-attended feature, we incorporate a self-attention mechanism into the feature map Fself, formulated as:
F Fself SelfAAtention(FFrae)
To combine the outputs the cross- and self-attention mechanisms, the final fused features are computed as:
Fvisuat FM 72 (Feross Y1 Fsets).
(8)
where y1. 72 are the learnable vector.
Text Prototype Guidance (TPG). To fully leverage the prior knowledge embedded in rPPG signals and visual features, aim enable these modalities play critical role in the task. However, rPPG signals and visual features are neither directly editable nor easily describable in natural language without missing information, which poses significant challenges for guiding LLMs to understand temporal and visual features without resource- intensive fine-tuning. Specifically, the non-discrete and high-dimensional nature of such data makes it difficult transform them into symbolic representations suitable for language models.
shown in Fig- c), to bridge this features with word embeddings E R gaD, we propose to reprogram the rPPG signals and visual where V is the vocabulary size. Nevertheless, there is no prior knowledge indicating which source tokens are directly relevant. Thus, simply leveraging E will resulting large and potentially dense reprogramming space. A simple solution is to maintain small collection of text prototypes by linearly probing, denoted as E' e ERV xD where V' V.
The text prototypes E' are then used to enhance the interaction between rPPG signals, visual features, and language model. To extract more sufficient details from the input video and signal, we devise block, which consists of multiple different transformer layers. Specifically, given input feature X
Xself SelfAttention(X).
E fusion E' Xself-
(10)
EE Juston: X) CrossAttention E Fuuiion X. X):
(11)
Output -E' E'jusion + (2(EE jusion' X),
(12)
Tout FFN(E))
(13)
where E and E, are the updated versions of E'. It is worth noting that the TPG module guides not only visual features, but also sequence features, the input feature X here has the meanings. In
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addition, the visual feature part and the sequence feature part share the same TPG module to learn potential associations.
For simplicity, represent this sequence of operations single function TPG()), so the output of this module can be expressed as Tou = TPG(X).
3.2 PHYSIOLOGICAL CUE-AWARE PROMPT LEARNING
The extraction of reliable rPPG signals from facial video data presents significant challenges due varying lighting, subject mobility, and diverse skin tones in complex real-world scenarios. To address these challenges through cross-modal learning, we propose a Physiological Cue-Aware Prompt Learning framework that generates rPPG sample-specific cues for guiding adaptive token learning.
Cue Caption. Prompt-based adaptation en- ables LLMs to address specialized subtasks without requiring parameter updates, as demon- strated by the zero-shot capabilities of architec-
Cue Caption
(8)
Describe the kes fackal festures, sktn tose distrilation, and any orgiares officting IPPG sipnol quality.
The inag katuo youu toas with weating pland sart.
Cyision
tures like LLaMA (Touvron et al. (023) and
LLaVA
Despbech
DeepSeek (Guo et al. 2025). To eliminate manual annotation of context- aware descriptors,
(b)
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Crssk
Peqöeck Takenber
leverage LLaVA (Liu et al.) 2023a) for au- tomated physiological cue generation through structured prompt engineering,
(c)
shown in Fig. b(.). For rPPG-specific adaptation, our prompts focus on three critical visual domains: I) facial anatomical characteristies, 2) transient emotional expressions, and 3) environmental il- lumination dynamics, as exemplified by our de- signed query templates in Fig.
minn
*Stats Cuu-rlppe stativiies (min, valuse ser), [man, values, str). [median values, stt) ahe trend of lepas is ('agward aod if [rtendbbb] > clse "awweweed)
DrepSert
seats
Introduction to the composition of cues. (a) Extracting visual priors (c.g.. lighting, facial expres- sions, occlusions) via LLaVA and encoding them into visual tokens. (b) Tokenizing textual descriptions of the rPPG task to derive task-specific priors. (c) Analyzing statistical features of rPPG signals from the backbone network to generate statistical prior tokens. These cues synthesize visual, semantic, and statistical priors for hanced physiological signal analysis.
Figure 4:
Formally, given an input video sequence V e R3XTXHXX we extract the central frame I, and process it through LLaVA with task- oriented prompts to obtain detail descrip- tion, and tokenize the description, Cvision T (okenizer(LLaV A(,, Q)), where Q denotes physiological cue queries.
Furthermore, we formalize domain knowledge through task-specific primers derived from consensus deseriptions in rPPG literature. These primers encoded into L.L.-coppatblle tokens via the model's 's native tokenizer:
Clask Tokenizer(P),
(14)
where represents the standardized task preamble.
While traditional text-based prompting strategies have demonstrated effectiveness in general vision- language tasks, we identify critical limitations when applied to physiological signal estimation: (I) Static visual descriptors cannot sufficiently capture temporal hemodynamic variations, and (2) Task- agnostic queries fail to address rPPG-specific contextual dependencies. To bridge this semantic gap, propose statistical priors derived from pretrained rPPG models physiological knowledge supplements. Although ground truth signals remain unavailable during inference, the pretrained network's 's output sequences (discussed in Section 3.1 preserve valid distribution characteristies. So the statistical cue S is derived from the pretrained model's rPPG signal projections xene e BBxT where B denotes batch size and 7 the temporal dimension. For each sample E [1, B], we compute,
S min(xend [b. (nin(xene(bb.)))
max (oxxxe)e(bb.)). median(xene(ba (b.:)).
(15)
[(Xene [b.:]) sgm Σ
(Nene 11 (b,r)) TopK ((, 1=1
where r'() calculates signal trends through first-order differencing, [(x) E/ x;- Then S will make up the symbolic representation as described Fig. c). Finally, the tokenization process is formulated as Creats Tokenizer (S).

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Table 1: Intra-dataset testing results on UBFC-RPPG (Bobbia al. 2019). PURE (Stricker al,
2014 BUAA (Xi et al. 2020 and MMPD TTngg et al. 2023) datasets. Best results are in bold.
Method
UBFC-rPPG
PURE
BUAA
MMPD
MAE]
RMSEI
RJ
MAEE
RMSEJ
MAEL
RMSEL
MAEL
RMSEJ
R
ICA (Poh et al. 2010
16.00
25.65
0.44
4.77
16.07
0.72
18.60
24.30
0.01
CHROM de Timin & Jeanne 2013
4.06
8.83
0.89
5.77
14.93
0.81
13.66
18.76
0.08
Green
Verknnssse
19.73
31,00
0.37
10.09
23.85
0.34
6.89
10.39
0.00
21.68
27.69
-0.01
POS Waig e mln 2017
4.08
7.72
0.92
3.67
11.82
0.88
12.36
17:71
0.18
Metu-PPPG
0.57
PhysNet
ef 20TV
2.95
3.67
0.97
2.10
2.60
0.99
10.89
11.70
-0.04
4.80
11.80
0.60
PhysFormee
0.99
1.75
8.46
0.18
EfficientPhys
Lii al.
1.41
1.81
0.99
4.75
9.39
0.99
16.09
16.80
0.14
13.47
21.32
0.21
Contrast Phys+
2174
0.99
0,98
RhythmFormer
200 er
0.50
0.78
0.99
0.27
0.47
0.99
9.19
11.93
-0.10
4.69
11:31
0.60
(OarS
0.21
0.57
0.99
0.17
0.35
0.99
6.48
8.48
0.63
4.36
10.76
0.65
Adaptive Prompt Learning (APL.. Conventional multimodal fusion approaches typically em- ploy static weighting coefficients for prompt integration, suffering from two limitations: (1) fixed combination ratios cannot adapt learnable LLM, and (2) inability to adaptively select the key cues across different data. As shown in Fig. a), our adaptive fusion paradigm addresses these constraints through learnable hierarchical composition. Formally, having obtained three fundamental cue cap- tions C = [Crask. Cvision Cstais), we first implement modality-specifft conditioning via dedicated transformation networks,
& AttentiveCompressorr (Cx), k E task, vision, stats }.
(16)
where each compressor hwtpppreeeeseempplsslsseompppooo
attention to maintain modality--pecciiccatte
Q
AttentiveCompressor, (x) Softmax
Vx.
(17)
va
with Qx. K2, Vy derived from x through linear projections.
In addition, the adaptive fusion mechanism learns three independent parameter matrices:
[Wask. Wvision- Wstass
3xxxxxx
(18)
where L predefined hyperparameter about target prompt sequence length, d is the token embedding dimension, and Wissk, Wvision, Wstats denote learnable tokens, respectively.
The complete fusion operation is formalized as:
Tewe Σ ( Ek. Q A (rask, vision, stats). RE
(19)
3.3 TrAINING OBJECTIVES
we obtain the stationary signal & and the fused multi-scale visual features Fvisual through the DDS module and multi-scale interaction, respectively. Through TPG, we obtain the text-guided vision token Trision = TPG (Fvisuat), and the text-guided signal token Taignal = TPG z)) Then, we input Teue Trision Tsignal into LLM and predict waveforms Finally, we employ the mean square error loss function as the total loss,
In Section 3.1
LMSE Σ (yr (,)-
(20)
where y is the ground truth PPG signals.
EXPERIMENTS
4.1 DATASETS AND PERFORMANCE METRICS
We comprehensively evaluate models on four benchmark datasets (UBFC-1PPG (Bobbia et al. 2019), PURE (Stricker et al. 2014), BUAA (Xi et al. (020),, and MMPD (Tang et al. (023 Following (Sun & 2022 Yu et al. 2019), we calculate mean absolute error (MAE). root mean square error (RMSE), and Pearson's s correlation coefficient (R) between the predicted HRs versus the ground-truth HRs as evaluation metrics. Notably, for MAE and RMSE, lower values indicate reduced error margins, whereas for R, values approaching 1.0 signify diminished enror. Among them, both MAE and RMSE are measured in terms of bpm (beats per minute). More implementation details can be found in Appendix.
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Table 3: Cross-domain generalization evaluation Table 2: Cross-domain generalization evaluation with three-source training and single-target test- with dual-source training and single-target testing. ing.
Method
P+B 1
P+U
M
B+C
Method
Others + MMPD Others
BUAA
MAR]
RMSE]
MAR]
RMSEL
MAR]
RMSEL
MAE]
RMSEL
MAEL
RMSE
Varkmyyse
2008
21.7
21.7
21.7
Green Vorknuysse al. 2008
21.7
27.7
FificienPPyys (io at 1022
PhysFormer al
PhyaNer
Youe al. 2019
139
11.9
132
16.7
18.5
11.8
11.0
18.9
17.3
13.2
15.5
19.5
20.8
17.0
EfficienPPhys Plys (Lu et 2002
PhysFormer Ya et 2022
13.2
13.9
20.02
19.3
32.3
7.7
12.4
34,0
RbythatFormer
1025
13.96
10.5
12.6
PhysNet eY 2019
12.8
Pbyst.LM (Qurs)
11.9
153
9.95
14.96
12.1
15.2
RhythnFormet Zou et al 2025
16.1
20.5
6.04
10.8
Physl. (Ours)
12.2
15.5
6.01
8,6
4.2 INTRA-DATASET TESTING
We first evaluate the HR estimation on all datasets under intra-dataset setting. We compare our method with 10 methods, including traditional methods and deep learning methods. Table I displays intra-dataset testing results for UBFC-rPPG (Bobbia et al., 2019), PURE (Stricker al. 2014). 4),
BUAA (Xi et al., 2020) and MMPD (Tag et al., 2023).
HR Estimation on UBFC-rPPG (Bobbia et al., 2019). On UBFC-IPPG (Bobbia et al. 2019). we 2024 by training on the first 30 subjects and testing on the remaining 12. As shown in Table LLysLLM achieves state-of -of-the-art art HR estimation with MAE 0.21 bpm, RMSE 0.57 bpm, and R 0.99. These results indicate stable rPPG fitting and effective long-term HR tracking.
follow (Luo et al.
HR Estimation PURE (Stricker et al., 2014). Following (Luo et al. 2024), we compare PhysLLM with 9 methods. As shown in Table P achieves the best HR performance across all metries, outperforming the second- best PhymFrrmer et al. 2022) by 0.31 bpm MAE and 0.63 bpm RMSE, demonstrating strong robustness to head-motion interference.
HR Estimation on BUAA (Xi et al. 2020). is partitioned in sequence into training and test sets with ratio of 7:3. The performance of existing methods on this dataset is reimplemented by ourselves with the rPPG toolbox (Liu et al. (023c). The HR estimation results are shown in Table The proposed PhysLLM outperforms the existing state-of-the-an methods on MAE (6.48 bpm), RMSE (8.48 bpm), and R (0.63) metrics for HR prediction. The results show the strong robustness of L.ysLLMM against various lighting condition disturbances.
HR Estimation on MMPD (Tang et al., 2023). Following the protocol in Zou al. 2024). we compared our method against existing state-of-the-art approaches, as depicted in Table I The proposed PhysLLM outperforms the existing state-of-the-ar methods on MAE (4.36 bpm). RMSE (10.76 bpm). and R (0.65) metrics for HR prediction. These results demonstrate that PhysLLM performs well under
conditions.
4.3 CROSS- DATASST TESTING
We also perform cross-dataset testing assess the generalization capability of PhysLLM.. We conduct both Two-training and One-testing protocol and Three-training and One-testing protocol. In each case, we train on relatively simple datasets and evaluate performance on one more complex dataset.
Dual-source training and single-target testing protocol. To evaluate generalization, we train on two datasets and test on the most challenging MMPD (Tang et al. (023). For instance, P U - M denotes training on PURE (Stricker et al., 2014) and BUAA (Xi et al.) 2020) and testing on MMPD using the full datasets. As shown in Table 2 PhysLLM remains best under these cross- domain settings, reducing MAE/RMSE by 1.05/2.33 bpm on P U - M. demonstrating strong generalization enabled by visual context and LLM modeling.
Three-source training and single-target test- ing protocol. To further evaluate whether PhysLLM can learn domain invariance knowl- edge without mixing domain- specific knowl- edge, we trained on three datasets and tested on the remaining one dataset. For example, Others + MMMD means training on the other datasets (UBFC-IPGG (Bobbia et al.) 2019).
Table 4: Ablation results of the main components on the UBFC-IPPG (Bobbia et al. 2019) dataset.
Method
UBFC-PPG
MAR]
RMSEL
R1
0.36
1.12
0.98
0.41
1.26
0.98
x
x
/
0.32
1.00
0.98
d
x
0.27
0.92
0.98
PURE (Stricker et al. (2014), BUAA Xi et al. 2020 and test on MMPD (Tang et al.| (2023).
0.34
1.05
0.99
0.25
0.76
0.99
It worth noting that only tested two dif-
0.21
0.57
0.99
ficult datasets: BUAA (Xi et al. 2020) and
MMPD Tang al. 2023 It can be seen from the results in Table
3 that PhysLLM achieves
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Table 5: Ablation experiments of different large language models and transformer baseline. While DeepSeek used the default LLM our experiments, the proposed approach can be easily generalized to other large language models.
Method
LLM Param.
UBFC
PURE
MAEL
RMSEL
MAE]
RMSEL
PhysNet YYu et al. 2019)
2.95
3.67
2.10
2.60
PhysLLM (w. Sundial (Liu et al. 2025
0.92
2.46
3.22
7.54
PhysLLM (w. DeepSeek (Cuo et al. 2025
1.5B
0.21
0.57
0.17
0.35
PhysLLM (w. Bert (Devlin al. 2019
0.11B
0.19
0.76
0.43
0.80
PhysLLM (w. GPT2 (Lagler al. 2013
0.124B
0.19
0.76
0.14
0.35
the best performance in both datasets, and even outperforms the existing state-of-the-and methods on
MAE (6.0 bpm), RMSE (8.6 bpm), and R (0.63) metrics for HR prediction on Others BUAA. This shows that PhysILM will not confuse domain-specific knowledge even if trained on three datasets, and can stably learn domain-invarian rPPG knowledge.
4.4 ABLATION STUDY
Impact of Dual-Domain Stationary Algorithm (DDS). The role of DDS is to transform the input signal x into a stationary signal Z(x). To validate the effectiveness of the DDS, we construct comparative experiment designed to remove DDS. Specifically, we directly use the rPPG signal from the video encoder, instead of the rPPG signal smoothed by DDS. The results are shown in the sixth row of Table 4 DDS reduces the MAE by 0.04 bpm, and the RMSE by 0.19 bpm on PURE dataset.
Impact of Vision Aggregator (VA). In this ablation study, we simply remove the Vision Aggregator evaluate its impact on the model. As shown in the results UBFC-IPPG dataset, the absence of the Vision Aggregator leads to a noticeable decrease in performance, underscoring its importance in the model architecture. The comparison between the fifth and seventh rows Table 4 clearly demonstrates that the fusion of visual features is effective.
Impact of Text Prototype Guidance (TPG). To evaluate the effectiveness of text prototype guidance, conducted an ablation study by removing the text prototype guidance. The results on UBFC-IPPG dataset are shown in the fourth row of Table 4 TPG brings significant decrease in MAE (0.06 bpm) and RMSE (0.35 bpm). These results underscore the importance of TPG in facilitating the fusion of quasi-periodic video features.
Table 6: Ablation study of prompt components on the UBFC-IPPG (Bobbia et al. 2019) dataset.
Impact of prompt components. To evalu- ate the contributions of different prompt com- ponents (Vision, Statics, Task, and adaptive learning) in PhysLLM's cue caption and adap- tive prompt learning, we conduct an ablation study and report MAE/RMSE in Table 6 The full configuration achieves the best performance (MAE 0.21 bpm, RMSE 0.57 bpm). yielding 76% improvement over the worst setting (MAE 1.31 bpm, RMSE 10 bpm) that removes adap- tive learning, confirming its critical role. We also observe that adding the Task component generally improves performance, and Vision+Task outperforms Statics+Task, suggesting visual prompts better align with rPPG-specific cues.
Method
UBFC-rPPG
Vision
Stats
Task
Adaptive learning
MAR]
RMSEL
0.83
1.53
d
x
0.71
2.26
x
x
0.84
2.41
x
0.73
2.29
/
0.43
1.20
0.61
/
1.31
2.10
/
0.21
0.57
Ablation Study on the LLM Component. To validate the necessity of using a pre-trained LLM, we compare three LLMs with varying sizes: DeepSeek (Guo et al. (025). Bert (Devlin et al.) 2019). and GPT2 (Lagler al. 2013). As shown in Table 5 all LLMs achieve competitive performance, with DeepSeek performing best (MAF: 0.21/0.17 on UBFC/PURE) We further replace the L.M with Sundial (Liu al. 2025), temporal transformer without LLM pre-training. The significant performance drop (MAE: 3.35/4.05) demonstrates that the LLM's pre-trained knowledge is essential for cross-datase generalization, not merely the transformer architecture.
4.5 VISUALIZATION AND DISCUSSION
Robustness Analysis on Skin Tones and Lighting Conditions. To evaluate robustness. we perform stress tests the MMPD (Tang al. 2023) dataset by varying skin tones (Types 3-6) and lighting

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Table Performance Comparison across Different Skin Tones and Lighting Conditions on MMPD (Tag et al.)
Table 8: Comparison of Model Complexity
2023 dataset (MAE/RMSE).
Method
Params (M)MACs (G)
(a) Performance across Different Skin Tones.
TS-CAN Liu 2020
7.5
96.0
Method
Type
Type
Type 5
Type
PhysNet Yu ot
2019
0.77
56.1
PhysFormer Yu al.
2022
6.11/11211
5
5.87/13.12
6.11/1121
DeepPhys Chen & McDuff
2018
7.5
96.0
Rhythmoormeer Zou a.
2025
5.12/11.25
5.46/9.14
6.32/1187
6.26/9.99
EfficientPhys
2021a
7.4
45.6
PL (Ours)
4.96/10.27
4.73/8.49
4.99/1107
5.73/8.49
PhysFormer
ct al
2022
7.38
40.5
(b) Performance across Different Lighting Conditions.
RhythmFFormer
Zou
2025
4.21
28.8
Method
LED-Low
LED-High
Incandescent
Natural
Contrasi phys+
145.7
Poymeormer eu al.
2023
6.36/11.72
5.12/9.39
5.71/12.73
6.61/11.36
PhysMamba Luo et al.
2024
0.56
47.3
RhythmlFormer Zou a. 2025
5.85/11.71
4.46/8.98
3.64/11.87
5.65/12.31
PhysLLM (Ours)
4.46/9.57
3.72/8.10
3,5911443
3.45/6.81
PhysLLM (Ours)
97.2
424.3
conditions (LED-Low/High, Incandescent, Natural). As shown in Table 7 PhysLLM consistently outperforms PhysFormer and RhythmFormer across all settings, especially under extreme lighting (MAE/RMSE: 3.45/6.8 in natural light) and diverse skin tones (4.73/8.49 for Type 4). Overall, PhysLLM remains stable under challenging variations, supporting its real-world applicabiliy across different populations and environments.
Model Complexity Analysis. As shown in Table g PLysLLLM has higher computational complex- ity (97.2M parameters, 424.3G MACs) due the LLM backbone. While we acknowledge this overhead, it is necessary trade-off for achieving superior cross-dataset generalization and robust- ness demonstrated in our experiments. Future work will explore model compression techniques such knowledge distillation and parameter-efficient fine-tuning to reduce costs while maintaining performance for resource-constraineed deployment.
Visualization of saliency maps. Fol- lowing (Sun & Li 2022). the saliency maps of the PhysLLM are visual- ized in Fig. 5 using samples from UBFC-IPPG (Bobbia et al.] 2019) and PURE (Stricker et al., 2014) datasets. The more obvious the red-green area, the more attention means. We se- lected images with certain features from the dataset to test rPPG tasks, such as head rotation, with noticeable beard, darker skin tone, and wearing glasses. From the test results, can seen that PhysLLM can effectively capture rPPG-related parts. For example, the observable parts are concentrated on the cheeks and forehead, which is consistent with relevant prior knowledge of rPPG. At the same time, relevant parts can effectively avoid hair and obstruction. For example, there is no prominent attention in the area where the hair appears, and even subtle changes in the neck can be observed. This is sufficient demonstrate the robustness and effectiveness of PhysLLM in various scenarios.
(A) PURE
UBFC-PPG
Figure 5:
Visualization of salieney maps from PhysLLM PURE and UBFC-rPPG datasets.
CONCLUSION
In this paper, we propose the PhysLLM, a framework that integrates LLMs with specialized rPPG components. The Text Prototype Guidance strategy bridges the cross-modal gap, while the Dual- Domain Stationary Algorithm addresses signal instability. Task-specific priors enhance adaptability in challenging scenarios. Experimental results across four datasets show PhysLLM achieves superior accuracy and robustness, particularly under variable illumination and motion. Future work includes improving mods- alignment and developing lightweight models for edge deployment.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Grant No. 62306061, I, 62576076, and 62276170) the CCF-Tencent Rhino -Brd Open Research Fund, the Basic and Applied Basic Research Foundation of Guangdong Province (No. 2021B15 (15111440031 and the Dongguan So- cial Development Science and Technology Project (Grant No. 20221 800906352, 20221 1022180090361,, 2021 1 1800003402 and 2023180000 8009360772. This work was also supported by the Science and Tech- nology Development Fund of Macao (No. 0009/2025/TTP) and the Macao Polytechnic University Grant (RP/FCA-16/200) Additionally, this work was also supported by the Intelligent Computing Center of Shenzhen University.
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A INTRODUCTION TO THE DATASETS
UBFC-rPPG (Bobbia et al. 2019) contains 42 RGB facial videos from 42 distinct subjects. Each video is captured at 640x480 pixel resolution and 30 frames per second (fps). Recordings take place under varied lighting conditions, including natural sunlight and indoor artificial illumination. Ground-truth physiological signals recorded via CMS50E pulse oximeter 60 Hz, ensuring precise temporal alignment for evaluation.
PURE (Stricker et al. 2014) comprises 60 high-quality RGB videos collected from 10 subjects per- forming six different head movement scenarios (stats, talking, translation movements, etc.). Videos recorded 30 fps under consistent indoor lighting and controlled background settings, minimiz- ing external interference. Synchronized physiological measurements are obtained using a CMS50E oximeter sampling at 60 Hz. PURE is particularly valuable for evaluating rPPG performance during facial movements.
BUAA (Xi al., 2020) designed assess algorithmic robustness across varying illumination intensities. The dataset features video sequences recorded under range of controlled lighting conditions, from low-light (below lux) to normal brightness. In our experiments, we only utilize videos captured under illumination levels 2 10 lux, as extremely dim lighting introduces significant image degradation requiring specialized enhancement techniques beyond this study's ' scope.
MMPD (Tang et al., 2023) comprises 660 videos, each lasting one minute, collected from 33 subjects with diverse skin tones and gender distributions. Each video recorded at 30 fps with a resolution of 320x240 pixels, under four distinet lighting conditions (bright, warm, dim, and colored lighting). Subjects perform various daily activities, introducing intra-subject variability and further increasing dataset complexity.
B IMPLEMENTATION DETAILS
We conduct experiments on Pytorch and mainly based on the open-source toolkit rPPG-Toolbox (Liu al. (023c). For data prepprocessing, we crop the face region in the first frame for each video clip and fix the region box in the following frames. Subsequently, we randomly sample a video chunk of 128 frames and resize them into 128 128 pixels. We use the default hyperparameters settings
0.8 and harget = 32. The backbone of our PhysLLM is based on the pre-trained PhysNet (Yu et al.) 2019). where the training strategies follow the methodology outlined in the original paper. In the intra-dataset testing and ablation study, use the same training data for PhysNet as for PhysLLM, ensuring consistency in the data used. For cross-domain experiments, we use the pre-trained PhysNet model trained the PURE dataset (Stricker et al. 2014), ensuring test set overlap and consistent pre-training conditions. Furthermore, we use the DeepSeek- 1.5B (Guo et al.) 2025) version as the I.M.. The PLy is trained with Adam optimizer and the initial learning rate and weight decay are le-4 and 5e-5, respectively. We train our model for 20 epochs on a NVIDIA A100 GPU with batch size of
C MATHEMATICAL PROOF OF STATIONARITY
To prove that the outputs of the BDCS algorithm are stationary, we first verify the stationarity of the core Stationary Algorithm, then extend this proof the time-domain and frequency-doman outputs, and finally conclude the global stationarity of the combined output.
STATIONARY ALGORITHM STATIONARITY
We need verify the three conditions for stationarity in the output of the Stationary Algorithm Z(x):
Zi = xx (1 az)
ar)..)a. Zi-1
= ar x; + (1 - (r) - (a xi-1 + (1 (ax) -x) z4-2)
a a. a) xi-1 (1=a)2.. 2
(21)
20
= Σ
a(1 a)² xi-k
k=
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Algorithm DDS
Input:
Time series signal R wavelet basis v decomposition level J. Stabilized signal ? Rxxa
Output:
Define S(x,)
11 I S XI
0 - El=1 (xi -H)
return 0++
// Time Domain Decomposition
Define (x/) a x/ + +1 (1-a). Zj-1
xy - S(x;) // Standardization
time xn
20
for = to n do
(x,)
end for
Frequency Domain Decomposition
Kaci Ixde.l..... < DWT(x, J) // Wavelet decomposition
Xae S(xac), de.j (( for j l.....J
Zac + Z(x'ac) Zdo,) + Z (x'de..) for j l.....J
=fre IDWT(EEc [Tde,I.... Ide, sl, (b) // Inverse wavelet transform
// Adaptive Fusion
-)) zlime B zie
return 2
where x; series with a mean of and variance of If s xi-k 0
1. CONSTANT MEAN
The normalized signal x; has mean:
E[x,] = 0.
(22)
The smoothed signal z; is defined as:
21 a a) xi-k
(23)
Since B[y()] 0, mean of z; is:
a(1 a0) - =0.
(24)
k=
Thus, the mean of is constant and equal to zero.
2. CONSTANT VARIANCE
The variance of is given by:
Var(z;) = =[ 2.
(25)
Since B[2,] 0, we have:
Var(z,) = =[
(26)
Var(z)) E
Σ
a a)* x1-k
(27)
k=0
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Published as a conference paper at ICLR 2026
Assuming Xy-k has unit variance (Var(xy-k) 1) and different time points of x;-X uncorrelated:
20
(a(1 - w) (a(l-a)f)
Var(z;) =
Var(xi=2).
(28)
Since Var(y(r)) I, we have:
Var 2/) aa aa(1-a)
(29)
This is geometric series with sum:
Σ k=0
(a(1-a)))
a²
(1 - aa 2k
k=0
a²
(30)
1- (1 (1-a) 0)²
-
Thus, the variance of z is constant.
AUTOCORRELATION DEPENDS ONLY ON TIME LAG
The autocorrelation function zi is defined as:
(T(T) B[zizi+r].
(31)
Substituting Z = Ik= a(1 - a)* xi- and ž/4r = Eiz0 a(1-) a)) - x14T-ji
R₂(T) =
Σ a - a)) -xi-k
(32)
50
Σ a(1 a) -XHT-1
Expanding the product:
60 80
(T( ΣΣ
21 x)* B[xy-xxer--]--
(33)
k=0 J=0
Since y(1) zero-mean and uncorrelated different time points:
if i
B[x = I otherwise.
(34)
This implies that the autocorrelation depends only on the time lag r, not on the specific time r. Thus, (T) satisfies the condition for stationarity.
See the specific algorithm in Algorithm I
D COMPARISON BETWEEN PLYSLLM, CNN-LLM HYBRID, AND TRANSFORMER BASED METHOD
We supplement study with comparative experiment between PhysLLM and alternative ap- proaches in Fig. 6 First, we compare with a CNN-LLM Hybrid approach, which refers to utilizing the same pretrained PhysNet encoder to extract features, which then fed into the LLM for further
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Published as a conference paper at ICLR 2026
I PhysLLM (Ouns)
CNN-LIM Rybrid
Transformer based
PhyALLM (Ours) II CNN-LLM Hybrid Transformer based
18.5
16
15.4
15.8
14
115
14.9
12
12.2
12
10
P-B-M
P-U-M
U-B-M
Others -MMPD
Others -UAA
Figure 6: Comparison between PhysLLM, WNN-LLM and Transformer based method. The lower the MAE, the better the effect.
analysis. We adopt the same cross-domain evaluation strategy as in the main text, following both the Two-training and One-testing protocol and the Three-training and One-testing protocol. The experimental results demonstrate that our approach significantly outperforms the direct combination method and also characterize the effective integration of our methods.
Furthermore, we investigate the effectiveness of using LLM architecture compared to a Transformer- based approach. As shown in Fig. [6] while the Transformer-based method achieves competitive performance, our PhysLLM still demonstrates superior results across different evaluation protocols. This comparison validates our architectural choice of using LLM over simpler Transformer models, as the more sophisticated LLM structure better captures the complex physiological patterns and their relationships in the data, leading to more robust and accurate predictions in cross-domain scenarios.
E EXAMPLE OF GENERATED VISION CUR.
To validate the effectiveness of the generated vision cues, we compare the prompts generated by L.L.VA-base question answering (Liu et al.] 2023a) in our method with manually crafted prompts to investigate whether they correctly capture the knowledge required for the rPPG task. As shown in Fig. [0 we select four representative video frames, covering scenarios such as dim lighting conditions, head movements, facial occlusions due to beards and glasses, among others. From the prompts, it can be observed that the generated prompts effectively capture the desired details, such as gender, potential head orientation, background color, and accessories. Moreover, the generated prompts exhibit greater granularity; for instance, in the generated prompt of (a) in Fig. [0] they explicitly describe differences in lighting conditions. This comparison demonstrates the rationality and effectiveness of our
approach.
Furthermore, we visualize the BVP signals obtained from PhysLLM under the guidance of both the generated and manually crafted cues. The results indicate that prompts with finer details lead to more stable signals, further validating the effectiveness of our designed cue-guided prompting strategy.
F VISUALIZATION OF THE PREDICTED AND GROUND-TRUTH BVP AND PSD
We randomly select clip samples from UBFC-1PPG (Bobbia et al. 2019) and PURE Stricker et al., 2014] and plot the predicted rPPG and the corresponding PSD signals in Fig. 7 The results clearly demonstrate that PhysLLM effectively predicts the rPPG signals across different datasets and outputs the corresponding smooth waveform.
G ADDITIONAL ABLATION STUDY
Impact of the Hyperparameters. We have described all the configurations in Section 3, including the parameter of DDS and the length of the learnable prompt. These hyperparameters are selected based on experience. To verify that our chosen configuration for PhysLLM is suitable, we conduct ablation studies on the parameters a in the DDS, the length of each learnable prompt. The results are shown in Fig. 8 Note that only one parameter is changed at one time, while the others remain unchanged.
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Published as a conference paper at ICLR 2026
Waveform
PSD
mmape
100
100
340
140
200
100
100
175
130
Figure 7: Visual comparison of the rPPG signals (left) predicted by PhysLLM and their corresponding PSDs (right), alongside the respective ground-trun truth. (a) UBFC-rPPG (Bobbia et al. 2019). (b) PURE (Stricker et al. 2014.
344
RNESE Ogun)
RMIF (ym))
2.40
4.61
1.18
0.57
hergest
Figure 8: Ablation study of the hyperparameters of harger get and a in PLysLLM UBFC-IPG Bobbii et al
2019) dataset.
PlyliM
Phyt1M
PlydtMM
Rcossid gmo
347
Impact of Different rPPG Backbones.
To verify the generalization of our framework, in addition to PhysNet (Yu ctt al. 2019) as the backbone, we also consider two other backbones, namely
7.4M
1.01
1.M6
PhysFormer YYu et al. 2022) and Effi- eientPhys (Liu et al.] (021a). As shown in Fig. 9 the integration of PhysILMM consistently reduces the RMSE across all backbone architectures and datasets, highlighting its effectiveness. Specifi-
PlysNet
PlyoRomer UBBC
8fficientalys
PhyxaNo
Plyshommer
Efficientphhr
Figure 9: Impact of different rPPG backbones.
cally, the UBFC dataset (Fig (rig)a)). PhysLLM reduces the RMSE from 3.67 bpm 0.57 bpm for PhysNet, from 1.81 bpm to 0.91 bpm for PhysFormer, and from 2.46 bpm to 1.26 bpm for Effi cientPhys. A similar trend is observed RMSE from 2.6 bpm to 0.35 bpm for PhysNet, from 1.1 bpm to 0.96 bpm for PhysFormer, and from 9.39 bpm 1.34 bpm for EfficientPhys. These results clearly demonstrate that PhysLLM serves robust enhancement module that generalizes well across different backbone designs and data distri- butions. Notably, the substantial improvements on EfficientPhys, particularly on the PURE dataset, suggest that PhysLLM can compensate for weaker backbone performance and enhance reliability in more challenging scenarios.
the PURE dataset (Fig (b)), where PhysLLM lowers the
H ADDITIONAL RESULTS ON RESPIRATORY RATE PREDICTION
As shown in Table 9 we conduct comprehensive comparison of respiratory rate (RR) prediction performance three benchmark datasets: UBFC, PURE, and MMPD. The goal of this evaluation
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Published as a conference paper at ICLR 2026
Table Comparison of MAE and RMSE for respiratory rate estimation on the UBFC, PURE, and MMPD datasets.
Method
UBFC
PURE
MMPD
RMSE
MAE
RMSE
MAE
RMSE
PhysNet (Yu et al.
2019
15.82
17.84
13.78
16.46
10.30
13.70
PhysFormer Yu et al 2022
6.15
9.87
11.37
14.73
9.91
13.79
EfficientPhys (Luu ct al.
202 la
9.59
13.06
8.71
12.13
11.97
14.57
RhythmFormer Zou et al.
2025
4.16
7.89
7.83
11.89
6.37
8.89
PhysLLM (Ours)
4.05
7.70
6.66
9.32
7.03
11.38
to assess the generalizability and robustness of our method across diverse subject domains and recording conditions.
Our approach consistently outperforms prior state-of-the-art methods, including PhysNet, Phys- Former, and EfficientPhys, across all datasets. particular, we observe a substantial reduction in error on the UBFC dataset, where method achieves an MAE of 4.05 and RMSE of 7.70. Similarly, our method maintains strong performance on the PURE and MMPD datasets, outperforming others by a large margin.
These results demonstrate the effectiveness of our model extracting reliable respiratory signals from facial videos and generalizing different datasets. The improvements attributed model's 's robust spatiotemporal representation and domain-invariant design, which allow to maintain accuracy even under varying motion, lighting, and subject conditions.
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Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(d)
Prompt LLLVV:
The image features short-haired facing forward. He has prominent and small mouth. The facial regions show mix of skin tones,
(a)
appearing lighter impact the quality of the iPPG signal. The skin texture appears to be relatively smooth, and there no noticeable vascular parterss.
darker. There
artifaets, such uneven lighting shadows,
Manual prompt: The picture is a man with head slightly raised. He slightly yellow skin and has uncovered cheoks and forehead.
Prompt generated
The image features short-haired man with his head tumed to the lefl. He has prominent nose and a slightly receding hairline. The man's appears
(b)
significant shickws reflections that could affect the quality the :PGG signal. The man's facial fearures and skin texture appear to be relatively uniform.
noticrable
lighting image even,
Manual prompt: The picture of's man with his head slightly mised.. He has slightly yellow skin and has uncovered checks and foreheud.
Prompt generated by LLaVA:
The image fentures man with curly hair and beard, sitting fhont of green background He his prominent nose and slightly furrowed brow. There visible artificts, such lighting shodows, that could negutively impact rPPG signal quality. The skin texture appears to relatively smooth, and there are no noticeable vascular patterns.
(c)
Manual prompt: ambient light normal background green. looks down. on his chin.
Prompt generated by LLaVA:
The image features woman wearing glasses and scarf, siming front of green background. She has prominent nose and slightly pudgy face. The facial regions show
(d)
with forehead being lighter than the cheeks and The skin appears be relatively smooth, with no visible vascular patterns significant texture.
Manual prompt:
The ambient light normal and the bickground The woman looks down She has whiter akin and her check, inoovrre.
(a)
(b)
reing
ST-PRG
CT--PG
(c)
100
ids
(d)
100
SIT-PG
CIE-PR
Comparison between manual prompt and prompt generated by LLaVA Liu ct al 2023a). The top section presents the selected video frames, the middle section compares the generated vision cues with manually crafted prompts, and the bottom section illustrates the BVP signal predictions from PhysLLM, guided the two types cues.
Figure 10:
20


Reviews:
[{"review_id": "uiB7p6gtjD", "reviewer": "Reviewer_mjPp", "summary": "The paper proposes PhysLLM, a framework for remote photoplethysmography (rPPG) that couples a CNN-based rPPG backbone with an LLM through three key components: (i) a Dual-Domain Stationary (DDS) algorithm that stabilizes signals via complementary time- and frequency-domain smoothing with adaptive weighting, (ii) a Vision Aggregator (VA) that fuses multi-scale visual features using cross/self-attention, and (iii) Text Prototype Guidance (TPG) that aligns rPPG and visual features with a compact set of text prototypes to interface with an LLM.", "questions": "Please check the weakness part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tStrong empirical results. SOTA intra-dataset HR with gains on BUAA and MMPD. \n2.\tAblations and component evidence. Removing DDS/VA/TPG degrades metrics; including all yields the best performance, supporting the importance of each piece. \n3.\tTask priors + LLM prompting. The cue design (task, visual, static/statistical) and adaptive fusion are thoughtful ways to inject physiological context into an LLM.", "weaknesses": "1.\tJustification for LLM vs. sequence models. It remains unclear whether the LLM is essential beyond acting as a powerful sequence model; comparisons to strong non-LLM long-context baselines (e.g., state-of-the-art time-series Transformers without language pretraining) are missing. \n2.\tCompute/latency & deployability. The paper does not quantify training/inference cost (LLM size, parameter-efficient tuning specifics, throughput on typical devices) or real-time feasibility, which is critical for rPPG applications.\n3.\tData and evaluation breadth. While four benchmarks are covered, additional stress tests (e.g., extreme motion, low-light, occlusion regimes) and fairness (skin tone, age, gender) would strengthen claims of robustness and generalization.\n4.\tThe comparison with the previous rPPG using LLM shown below is missing.\n\nYue, Z., Shi, M., Wang, H. et al. Bootstrapping Vision-Language Models for Frequency-Centric Self-Supervised Remote Physiological Measurement. IJCV.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Rmukk21t23", "reviewer": "Reviewer_cdPR", "summary": "The paper proposes PhysLLM, a framework that integrates large language models with rPPG pipelines. It introduces several interesting modules, such as Dual-Domain Stationary signal stabilization, Text Prototype Guidance for cross-modal alignment, and Physiological Cue-Aware Prompt Learning for leveraging task-specific priors. The work is technically ambitious and attempts to bridge physiological signal modeling with vision-language reasoning.", "questions": "1. Could the authors clarify how much of the performance gain comes from the LLM integration itself, compared to the added DDS and TPG modules? An ablation or visualization isolating the LLM’s role would help.\n2. The evaluation does not include large and diverse datasets such as VIPL-HR or V4V. Could the authors comment on how PhysLLM would generalize to those more complex scenarios?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The idea of introducing LLMs into rPPG estimation is timely.\n2. The framework is clearly described, and the modular design (DDS, TPG, APL) is logically motivated.\n3. Writing quality and figures are generally good, helping the reader follow the methodology.", "weaknesses": "1. Limited benchmark coverage: the evaluation misses some widely recognized and more challenging datasets (e.g., V4V, VIPL-HR), which are important for validating cross-domain robustness in practical settings.\n2. The choice of LLM backbone (DeepSeek-1.5B) and its integration details are only briefly discussed, it is unclear whether improvements mainly come from the LLM or other architectural refinements.\n3. Some modules (e.g., APL and TPG) would benefit from clearer ablation or visualization to show how textual cues truly influence signal recovery.\n4. The cross-dataset generalization results, while improved, are still marginal and may not fully demonstrate strong domain invariance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dBDW04LoAj", "reviewer": "Reviewer_neJW", "summary": "PhysLLM introduces a novel framework for cross-modal remote physiological sensing by synergizing LLM with domain-specific rPPG components. The method tackles the challenge of extracting reliable physiological data from facial videos, which is often compromised by visual noise, lighting changes, and motion artifacts. Key contributions include a Text Prototype Guidance strategy for projecting physiological features into an LLM-friendly semantic space, and a Dual-Domain Stationary algorithm for stabilizing signals via adaptive time–frequency domain re-weighting. The results highlight the promise of combining LLM reasoning with specialized signal processing for resilient and interpretable health monitoring applications.", "questions": "1. Section 4.3's title needs to be rearranged.\n\n2. Why is PhysFormer++ excluded from several comparisons? Given its strong performance, including it in all datasets would strengthen claims of state-of-the-art accuracy.\n\n3. How robust are cue captions in unconstrained environments? What happens if visual descriptions are inaccurate due to occlusion, masks, extreme lighting, or motion blur?\n\n4. Since rPPG is typically real-time and mobile-oriented, can you provide inference efficiency metrics such as frames-per-second and VRAM usage?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel cross-modal architecture: The paper introduces PhysLLM, a framework that integrates text, vision, and physiological signals with ideas like Text Prototype Guidance and adaptive cue prompting, representing a meaningful conceptual advance for rPPG.\n\n2. Strong performance across datasets: It shows consistent improvements in both intra-dataset and cross-dataset generalization.", "weaknesses": "1. Lack of comparison on model size: Integrating LLMs significantly inflates parameter count and computational cost compared to prior rPPG methods. Since remote physiological sensing has strong real-time and mobile deployment requirements, storage and latency overhead are critical. The paper should provide model size, trainable parameter count, and inference efficiency comparisons with existing methods.\n\n2. Effectiveness of semantic info: The LLM backbone is positioned as the central source of improved robustness, yet the paper does not isolate the benefit of semantic reasoning from general long-sequence modeling. It remains unclear whether the LLM performs meaningful language-grounded reasoning, or primarily acts as a temporal encoder with additional embedding capacity. A comparison replacing the LLM with similarly sized non-linguistic temporal architectures (e.g., ViT-based rPPG backbones, GPT-style time-series forecasters) would be important to confirm language contributes beyond generic model scale.\n\n3. Statistical cue and prompt generation concerns: Cue captions depend on LLaVA-generated descriptions of a single frame, which may be noisy, biased by appearance (skin tone, beard), or misaligned with rPPG-relevant dynamics. The paper lacks ablations on prompt errors or failure cases (ex. when the cues are all incorrect and provide false information to the LLM).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dFhzLif30J", "reviewer": "Reviewer_K3Fm", "summary": "This paper presents PhysLLM, a framework that leverage large language models (LLMs) to improve remote photoplethysmography (rPPG) signal estimation. It involves a text prototype guidance strategy for promoting cross-modal alignment and a dual-domain stationary algorithm for solving signal instability.", "questions": "- Please improve the presentation of the paper, such as: figure 2 (e.g., what is input of video encoder; unclear figure 2a), poor-quality figure 7. There is even “??” in line 190, and error vspace in 401. \n\n- Discussion on the contributions (and possible experiments on accuracy and efficiency) as comparing the work with Time-LLM [1], VL-phys [2], PhysDiff [3] both in intra and cross-dataset. \n\n- Please add more discussion/justifications on Lines 48-50, which is currently vague on why do we need text. For example, many general textual context are not related: how a general text like “The image features a young man with a beard, wearing a plaid shirt. He is looking at the camera” can help the LLM training to output rPPG/HR using Deepseek model? \n\n- Following that, discussion on to what extent is it necessary to include LLMs usage on the text processing here for the task instead of earlier language models.  \n\n- Provide diverse end-downstream tasks. For example, extending the experiments more than just simple HR estimation would be a lot better (e.g., Question Answering or Reporting on more complex physiological parametters and health) at least can show more applications and more leverage the power of the given LLMs.\n\n[1] Jin, Ming, et al. \"Time-llm: Time series forecasting by reprogramming large language models.\" arXiv preprint arXiv:2310.01728 (2023). \n\n[2] Yue, Zijie, et al. \"Bootstrapping Vision-Language Models for Frequency-Centric Self-Supervised Remote Physiological Measurement.\" International Journal of Computer Vision (2025): 1-22. \n\n[3] Qian, Wei, et al. \"PhysDiff: Physiology-based Dynamicity Disentangled Diffusion Model for Remote Physiological Measurement.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 6. 2025.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Proposes an attempt on robust LLM-based cross-modal framework for rPPG signal estimation.", "weaknesses": "- Unclear architecture and presentation. Some figures and subsections flow are difficult to follow. \n\n- Weak justifications on the framework motivations, especially why there is a need to use two LLMs just to output only the single and straightforward task/output (e.g., rPPG/HR)? Isn’t it too much more energy and latency for this task? \n\n- The reported performance improvements on MAE in many settings/datasets (including ablation studies) are just minor (<1 bpm) so it seems to be physiologically insignificant relative to computational and architectural complexity.  \n\n- Lacks of diverse end-downstream evaluation, as only focus on simple HR task.  rPPG can be used for other tasks such as HRV, BP, stress detection, etc...", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)