From Pixels to Semantics: Unified Facial Action Representation Learning for Micro-Expression Analysis
Authors: Yicheng Deng
Hideaki Hayashi
Hajime Nagahara
Venue: ICLR 2026 Poster (2026)
Keywords: micro-expression recognition
micro-expression generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Micro-expression recognition (MER) is highly challenging due to the subtle and rapid facial muscle movements and the scarcity of annotated data. Existing methods typically rely on pixel-level motion descriptors such as optical flow and frame difference, which tend to be sensitive to identity and lack generalization. In this work, we propose D-FACE, a Discrete Facial ACtion Encoding framework that leverages large-scale facial video data to pretrain an identity- and domain-invariant facial action tokenizer, for MER. For the first time, MER is shifted from relying on pixel-level motion descriptors to leveraging semantic-level facial action tokens, providing compact and generalizable representations of facial dynamics. Empirical analyses reveal that these tokens exhibit position-dependent semantics, motivating sequential modeling. Building on this insight, we employ a Transformer with sparse attention pooling to selectively capture discriminative action cues. Furthermore, to explicitly bridge action tokens with human-understandable emotions, we introduce an emotion-description-guided CLIP (EDCLIP) alignment. EDCLIP leverages textual prompts as semantic anchors for representation learning, while enforcing that the "others" category, which lacks corresponding prompts due to its ambiguity, remains distant from all anchor prompts.
Extensive experiments on multiple datasets demonstrate that our method achieves not only state-of-the-art recognition accuracy but also high-quality cross-identity and even cross-domain micro-expression generation, suggesting a paradigm shift from pixel-level to generalizable semantic-level facial motion analysis. Code is available at https://github.com/KinopioIsAllIn/D-FACE.

Published as a conference paper at ICLR 2026
FROM PIXELS TO SEMANTICS: UNIFIED FACIAL ACTION REPRESENTATION LEARNING FOR MICRO- EXPRESSION ANALYSIS
Yicheng Deng, Hideaki Hayashi, Hajime Nagahara The University of Osaka
ABSTRACT
Micro-expression recognition (MER) highly challenging due the subtle and rapid facial muscle movements and the scarcity of annotated data. Existing meth- ods typically rely on pixel-Ievel motion descriptors such as optical flow and frame difference, which tend to be sensitive to identity and lack generalization. In this work, we propose D-FACE, a Discrete Facial ACtion Encoding framework that leverages large-scale facial video data to pretrain an identity- and Nomain-invarian facial action tokenizer, for MER. For the first time, MER is shifted from relying pixel-level motion descriptors to leveraging semantic-level facial action tokens, providing compact and generalizable representations of facial dynamics. Empirical analyses reveal that these tokens exhibit order-dependent semanties, motivating quential modeling. Building on this insight, we employ a Transformer with sparse attention pooling selectively capture discriminative action cues. Furthermore, explicitly bridge action tokens with human- undarssadnnds emotions, we introduce an deriiinnddssrrpiinnuuduiee
CL.IP (EDCLIP) alignment. EDCLIP leverages tex- tual prompts as semantic anchors for representation learning, while enforcing that the "others" category, which lacks corresponding prompts due to its ambiguity, re- mains distant from all anchor prompts. Extensive experiments on multiple datasets demonstrate that our method achieves not only oftte-of-the-are recognition accuracy but also high-quality tross-identig and even cross-domain micro-expression gener- ation, suggesting a paradigm shift from pixel-level to generalizable semantic-level facial motion analysis. Code is available GitHub
INTRODUCTION
Micro-expiie extro--xpression recognition (MER) a highly challenging task in affective computing because nicro-expressions. (MEs) generally subtle (Scherer (005), rapid (less than 0.5 seconds) (Yan) et a 2013), and performed on local facial areas (Josephs 2005). Nevertheless, due to the involuntary of MEs, their analysis is valuable in various applications that require uncovering hidden emotions, including lie detection (Ekman 2009), medical care (Endres & Laidlaw 2009). and
national security (O (Osuulivan et al.! 2009).
With the rapid development of deep learning, implementing accurate MER systems has become feasible. Early MER methods predominantly relied hand-crafted pixel-level motion representations, such as optical flow (Gan et al. 2019) and frame difference (Li et al. 2022), to capture the fine- grained facial muscle movements. More recently, some researchers have explored end-to-end frameworks by employing autoencoders to estimate optical flow and then designing unique networks for recognition (Zhai al. 2023). Nevertheless, hand-crafted optical flow remains the mainstream approach (Zhu et al. 2025
While these pixel-level motion descriptors can describe subtle and local facial motion, they suffer from two major limitations. First, they are highly sensitive to identity and therefore lack cross-subject transferability, as shown in Figure Ma). (). Second, they mainly describe displacement fields rather than the semantic meaning of facial muscle movements, which more relevant to expression interpretation. addition, the scarcity of annotated ME data further limits the robustness and generalization of deep learning models.

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TV-L1
Optical Flow
D-FACE Acton Tokens
Estimation
Optical Flowl
14
9
31
27
29
27
24
23
19
24
12
7
14
31
9
19
12
(a)
(b)
Figure 1: Motivation illustration. (a) Pixel-level motion descriptors (classical approaches) are identity- sensitive and suffer from poor generalization; (b) D-FACE encodes facial motions into action tokens, which are semantic motion representations that generalize across different identities and domains.
In this work, we aim to extract high-level semantic motion features that capture facial action occurs between two facial images, instead of relying on pixel-level facial motion features. We expect that such semantic representations are inherently generalizable enough to be shared across different identities, datasets, and even domains. Moreover, they offer the potential to generate new ME data.
To this end, we propose D-FACE, a Discrete Facial ACtion Encoding framework pretrained on large-scale facial video data, which uses the objective of the vector-quantized variational autoencoder (VQ-VAE) (Van Den Oord et al. (017) to discretize facial muscle movements between a pair of facial images into identity- and domain-invad invarinin facial action tokens for MER. That is, the facial action tokens generalizable to be shared across different identities and domains, thus serving vocabulary for representing facial actions. Through empirical studies, we observe that the facial action tokens exhibit order-dependent semanties and can be organized one-dimensional (ID) sequences, similar to sentences that describe the facial muscle movements. Motivated by this observation, we employ a Transformer with sparse attention pooling to emphasize informative local cues for MER. CLIP (EDCLIP) alignment to align the
unified
Furthermore, we introduce an penttionddescrpttoonnn learned action tokens with human- understandable emotions. Specifically, our textual prompts are constructed by combining each emotion name with its typical facial action descriptions, providing shared and consistent textual embedding for all samples of the same emotion and enabling a more explicit bridge between motion representations and emotional semanties. In addition, MER uniquely includes an ambiguous "others" category that cannot be precisely described through language. To address this, we adapt the CLIP mechanism by enforcing that "others" samples remain semantically distant from all emotion-specific textual embeddings rather than being aligned to an ill-defined description, allowing EDCLIP to better fit the characteristics of the MER task.
D-FACE achieves a paradigm shift for MER from observing pixel-level movements to modeling semantic-level action tokens. This is enabled by, for the first time, adapting VQ- (Q-VAE to MER through domain-specific considerations, carefully designed large-scale facial video pretraining, and extensive token-motion analysis that reveals interpretable and order-dependent token semantics. Extensive experiments demonstrate the effectiveness of D-FACE for MER and show that it learns a generalizable facial action representation, which extends beyond recognition and enables cross-subject and cross-domain ME generation.
Our contributions can be summarized as follows:
We introduce D-FACE, Discrete Facial ACtion Encoding framework pretrained on large- scale facial video data. For the first time, D-FACE provides a new perspective for MER by shifting from relying on pixel-level motion descriptors using semantic-level facial action tokens that are identity- and domain invariant.
Through empirical analyses, we uncover that the learned facial action tokens exhibit order- dependent semantics, motivating to model them as ID sequences similar sentences.
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nseet
Encoder
Acton Tokens
Decoder
19
12
11
20
27
Apex
21
23
28
21
25
Patch Embedding
Patch Embedding
I5 emantic movements of facial muscles (e.g.. raising eyebrows).
Apex
Flatten
Discrete Facial Action Encoding
Spatial Transformer
Spatial Transformer
[19. 9, 20, 27, 5. 21, 23, 28, 9, 21, 12, 25]
Corresponding latent feature vectors 2
Causal Transformer
Happiness S surprise Disgust
Transformer
Linear
Linear
Linear
Pul (General E motion!
Push (') (others)
Discretize
surprised expression with raised eyebrows and an open mouth." "A happy expression with...
Pretrained Encoder
codebook
EDCLIP
P Liinear
Micro EarroEEao Expression Recognition
(a)
(bb
Figure 2: (a) Overview of the proposed framework. (b) Architecture of the action encoder. The action encoder discretizes facial muscle movements between two input facial images into facial action tokens in unsupervised manner. These tokens are then flattened and processed by Transformer with sparse attention pooling for MER. Finally, imotiiondeserriiion-uuude CLIP alignment bridges facial action tokens with human-undee uumman--uderttaddblle emotions.
Based this insight, employ Transformer with sparse attention pooling capture informative action cues for recognition.
To explicitly bridge action tokens with human-umaan caanaan-undesttndablle emotions, we introduce an CLIP alignment, where textual emotion anchors regularize representation learning. The "others" category, which lacks appropriate textual prompts due its ambiguity, pushed away from other textual embeddings.
emotion- sereiionddesrripiio-g-uuee
Extensive experiments multiple benchmarks demonstrate state-of-the-ar recognition performance and superior cross-subject and cross-domain generalization in ME generation.
RELATED WORKS
Over the past years, researchers have explored variety of motion representations for MER. Early methods relied hand-crafted features and traditional methods for MER. Zhao & Pietikainen (0007) introduced Local Binary Patterns from Three Orthogonal Planes (LBP- TOP) as dynamic texture descriptor for MER. Wang et al. (2014) further proposed Local Binary Pattern Six Interception Points (LBP-SIP) for more compact representation.
With the development of deep learning. the combination optical flow and neural networks has become the mainstream. OFF-ApexNet (Gan et al.) 2019) computed optical flow between onset and apex frames and used CNNs for recognition. STSTNet (Liong al. 2019) designed triple-stream 3D CNN to capture multi-scale optical flow patterns. SLSTT (Zhang al. 2022) combined short- and long-term optical flow with Transformer and LSTM for temporal modeling. SRMCL (Bao et al. 2024) introduced prototype-base
contrastive learning with self-expressior reconstruction module to improve generalization. LTR3O (Zhu et al., 2202)) proposed a reduced three-frame structure (onset, occurring, offset) and calibrated its expressiveness via learning-to-rant More recently, autoencoder-
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style optical flow estimation architectures (e.g., FlowNet (Dosovitskiy et al.
RAFT Teed & 2023 Fan et al. 2023).
2015).
Deng 2020)) have been adopted for MER under self--upervision (Zhai et al.
Beyond optical flow, alternative pixel-level motion descriptors have also been explored. LEAR-
Net (Verma et al. 2019) compressed ME clips into a single dynamic image (Bilen et al. 2016) that
contained motion information. Graph-TCN. (Lei al. (020)) applied video motion magniticttion to
amplify subtle movements and extracted shape-based based features. MMNet eLi al. 2022) calculated frame differences between onset and apex frames the motion feature and showed effectiveness.
In contrast to these pixel-level motion descriptors, we propose to learn semantic-level facial ac- tion tokens, which are compact, identity- and domain-invariant, and thus enhance robustness and generalization for ME analysis.
3 METHOD
This section introduces our proposed D-FACE framework for MER. The overall architecture is illustrated in Figure a). Given the onset and offset frames of an ME, first discretize the facial musele movements into a semantic facial action token map. These tokens are then flattened into a ID sequence similar to sentences and modeled with Transformer with sparse attention pooling, which emphasizes informative local cues for recognition. In addition, we align the learned facial action information with uudaaanddddrtt emotions via emotion-desscritioon-uuude CLIP alignment. We next describe each component in detail.
3.1 DISCRETE FACIAL ACTION ENCODING
The encoder architecture is illustrated in Figure (b). Given two temporally related facial images I and I,, our objective is to discretize their difference into identity- and domain- action representations, which can be used to reconstruct I2 from I in an unsupervised manner. To this end, we adopt a conditional VQ-VAE (C-VQ-VAE) architecture originally introduced for latent action quantization in robotics (Ye et al. 2025). The original design in robotics employs a small codebook and short latent action length suitable for discrete control, inspired by the latent action model in Genie (Bruce et al. 2024). These learned tokens are subsequently used as labels for a token-oceurrence classification task for robotic control. In contrast, facial motion analysis requires modeling subtle and complex local muscle movements, which the original configuration canmot handle. Therefore, we adapt it to the facial domain by refining the codebook design and latent action length, and by conducting extensive token-motion investigations that reveal how tokens encode facial dynamics. Unlike general adaptation methods that introduce additional adapter networks (e.g.. Chen et al. (2024)), our adaptation is achieved through data- and analysis-driven redesign of the entire representation pipeline, enabling one-ggained facial motion extraction and understanding. We next describe the details of our framework.
Specifically, we first obtain the patch embeddings P1 and P2 via linear projection. Each embedding is processed independently with spatial Transformer to capture local spatial dependencies. The resulting features are subsequently passed through causal Transformer to model the directed temporal transition from I to Lg. A linear layer then reduces the dimensionality. and CNN is employed to yield embedding maps E,, E2 pxxwxxd' where h and denote the height and width of the embedding map, and the dimensionality of each embedding vector. The motion embedding is obtained the difference between E₁ and Ez:
D E2 EI.
We discretize D by mapping each feature vector to the nearest embedding from the codebook 2 ( =x} ) producing an index map M E Nixw The corresponding discretized action- token embeddings Z E ghxwxdd are obtained by looking up the codebook:
(M) arg min AE(I...., K}
[(D): 2x113
(Z), 2 ((M)
where E (1,..., h), (1, w.. These action tokens capture the semantic movements of facial muscles between I and Ig.
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During training, instead of directly reconstructing Ig from Z, NSVQ adopted before decoding:
Vali & Bäckström 2022 is
D D 7
V,
IV
where V E /xxxwxd is tensor whose elements follow (0, 1). This encourages discretized facial action tokens to remain close to the real difference. The latent representation D is then up- up-projected back to the feature dimension through a linear projection layer P to obtain D:
D = P(D).
(5)
A spatial-Transformer--bddd decoder D takes E, and D input to reconstruct the second frame Ig:
i. D(sg (PggsEE)) E)).
(6)
where sg[-] denotes the stop-gradient operator.
Finally, the reconstruction loss minimizes the pixel-level discrepancy between the original and reconstructed second frame:
Lce [1 - 121
The quantization process in the C-VQ-VAE introduces strong information bottleneck that forces the continuous motion features, obtained by differencing feature representations of diverse face pairs, be mapped into a limited data contains diverse set of facial identities with substantial variations in appearance and head pose (see Section 3.5 for pretraining details), a limited codebook cannot encode individull-ppeciff morphological differences (e.g., how "opening mouth" manifests in a specific individual's 's mouth shape). These identity and appearance variations lie much larger and more complex subspace than the low-dimensional discrete codebook space. As result, the optimization objective naturally encourages the quantization process to capture stable and commonly observed motion patterns shared across identities while filtering out identity- discretization to produce identity- and domain- invariant motion representations.
of shared codebook vectors. Since our large-scale pretraining
sepearance-specffe variations, enabling the
3.2
EMPIRICAL. ANALYSIS OF ACTION TOKENS
Given a pair of facial images, we obtain an ac- tion token map from the C-VQ-VAE, which pro- vides a compact and discrete representation of the facial muscle movements. To better under- stand how facial action tokens encode facial dy- namics for subsequent MER, conduct quali- tative experiments and present the visualizations in Figure (with more comprehensive analyses of token semantics provided in Appendix A. and A.2 We first input two identical facial images to the encoder to obtain an index map that corresponds "no action" between frames, Then, we selectively alter single index in the index map and generate new facial image.
Reference Face
S1
25
31
25
(a) Remain Unchanged
[b) Lid Tightener
(c) Eyebrow Raiser Lip Comner Puller
(d) Head Turn Left
+] aw Drop
These experiments yield two key observations. First, the same action token may activate differ- ent facial muscle motions when placed at differ- ent sequence locations, indicating that token's semantics depend on its sequence location rather than being globally fixed. Second, tokens located in upper regions can also drive mouth-related actions in the lower facial area, while tokens in lower regions may affect eyebrow movements in the upper facial area. This indicates that tokens at a given 2D position do not activate facial actions in the corresponding facial region, but are instead determined by their relative ordering and contextual interactions. Unlike CNNs that rely on local geometric adjacency to organize spatial semantics, our tokenizer composes motion representations through global attention across all sequence locations
Figure 3: action tokens through individual token alteration. The numbers in red stand for the altered indices.
Visualization of the empirical analysis of
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along with globally shared codebook. Consequently, movements can be controlled by manipulating a single token at a specific sequence location, rather than requiring the simultancous activation of all tokens across the 2D grid (see Appendix [A.2 for more detailed motion composition studies and visualization results). This motivates us to model the token map as 1D) ordered sequence using Transformer with 1D positional embeddings. We next detail this design.
global facial actions such head
3.3 TRANSFORMER-BANE BASDDD ACTION TOKEN MODELING
Motivated by our empirical observations, we flatten the discretized action-token embeddings Z as 1D sequences similar to sentences and then the linear projection layer P of the action encoder is used to obtain 2 (21, ....... 21,, where L h w. To incorporate spatial information, .....
add learnable positional embedding to each token. Formally, for the sequence 2, their embedded representations are given by
= T(2;) + Pss i i=,,..,,
(8)
where T()) is token embedding layer and p. R is learnable positional embedding.
The embedded sequence {g: is then processed by standard Transformer encoder lay- ers (Vaswani et al., 2017), each consisting of multi-head self-attention and feed-forward sublayers, which enable context-aware interactions among facial action tokens.
In order to aggregate the sequence into a compact representation for classification, we introduce sparse attention pooling mechanism. Specifically, given the output of the Transformer X Rxd we employ a global query vector E _d to calculate attention scores:
Xq
a softmax
Vd
where R² assigns importance weight to each token. The pooled representation is then obtained by
(x)))
(10)
Given the inherently local nature of MEs, only partial facial action tokens truly activate the facial muscle movements of an ME, while the remaining tokens may have little or no effect on facial muscle movements. Therefore, the model should focus on these informative tokens rather than distributing attention uniformly across all tokens. To encourage sparsity and focus on informative local cues, we regularize with entropy penalty:
1
Esparee
Σ
a log a,
(11)
L
im1
Finally, the pooled feature o is fed into a linear classifier for recognition.
3.4 EMOOTIO-DIICCAPTION-GCMMM CLIP ALIGNMENT
To explicitly bridge the learned facial action tokens with human- uumaaunndeestandablle emotions, we incorporate emotion-descriipiinn-ee -uided CLIP (EDCLIP) alignment. First, we design textual action description for each expression class, as illustrated Table These descriptions are encoded by frozen pretrained CLIP text encoder (Radford et al. 2021) obtain class-level embeddings [te]_] Given an input ME, the pooled action feature o is mapped into the same feature space via learnable function f(-). We then compute contrastive loss between the image and text embeddings to align the action tokens with semantic emotion descriptions:
exp (i))(f()),
LeuP log
(12)
Σ Ee exp (sim(f t.)T)
where t, is the text embedding of the ground-trut class, sim(-.) denotes cosine similarity, and is temperature parameter.

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Table Text descriptions provided for each emotion category involved in our experimental setup.
Emotion
Description
Happiness (Positive)
A happy (positive) expression with raised cheeks and pulled lip corners.
Surprise Disgust
A surprised expression with raised eyebrows and an open mouth.
A disgusted expression with lowered eyebrows, tightened eyelids,
a wrinkled nose, and a raised upper lip.
Repression
A repressed expression with depressed lip corners, and slight chin raise.
Negative
A negative expression with either lowered eyebrows, tightened eyelids, raised upper lip, depressed lip corners, or slight chin raise.
However, MER datasets often contain class, "others," which contains ambiguous MEs that are difficult assign a clear emotion label. For this special case, no textual prompt is provided. Instead, we encourage the representation to be dissimilar from all textual embeddings of the specific emotion categories via margin-basee based constraint:
oah max 0, max sim(f(o).t.) t.)) (,))
(13)
REC (Carh)
where 8 is margin haper-parameter, constraint ensures that the "others" representations are not aligned excessively close to any specific emotion description, thus preserving their inherent ambiguity.
set of all classes, and Coch is the "others" class. This
Therefore, the overall loss for EDCLIP is
CEDCLI LCLIP + XothCothh
(14)
where Aoth balances the contribution of Loth Finally, the overall training objective combines the classification loss, the reconstruction regularizer, the sparsity penalty, and the EDCLIP loss:
== Lets + Arec + Aupanse Exparse + AEDCLIP
(15)
where Lek denotes the cross-entropy loss for MER. Aress Asuarees and AEDCLIP empirically set hyper-parameters.
3.5 PRETRAINING DETAILS
We pretrain the C-VQ-VAE on the large-scale facial video dataset VoxCeleb (Nagrani et al. 2017). The VoxCeleb dataset consists of unconstrained interview videos collected from YouTube, where the frame rates vary depending the source video (most videos are around 25-30 fps). For pretraining, we randomly sample image pairs with a temporal gap between 8 and 15 frames, which approximately corresponds to the typical duration of MEs (0.25-0.5 seconds). This sampling strategy yields about one million facial image pairs from more than 7,000 distinct identities, producing rich diversity of motion patterns that greatly enhances the robustness of the learned facial action tokens (see Figure 9 in Appendix [A.4] for sample pairs). During pretraining, we train the model for 800,000 steps with batch size 64. The hidden dimension of the network and the dimensionality of feature vectors in the codebook are set to 512 and 32, respectively.
4 EXPERIMENTS
4.1 DATASETS AND EVALUATION METRICS
We evaluate performance under the leave-one-subject-out (LOSO) protocol using Accuracy, UAR, and UFI on four datasets: CASME-II (ann et al. 2014). SMIC-HS (Li et al. 2013). SAMM (Davison al. 2016), and CAS(ME) el. 2023). CASME-II contains 249 clips from 26 subjects recorded at 200 fps. SMIC-HS includes 164 clips from 16 subjects at 100 fps and 640x480 resolution; due to
the lack of apex annotations, the middle frame is used pseudo-apex. SAMM comprises 159 clips from 32 subjects at 200 fps and x040 1088 resolution. CAS(ME) provides 921 clips at 30 fps and 1280 x 720 resolution, making it one of the largest MER benchmarks.
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Table 2: Comparison with the state-of-the-art methods on the composite dataset under the CDE protocol (See et al., 2019). The best results are highlighted in bold and the second-bes best results are marked with an underline.
Methods
Full
CASME-II
SMIC-HS
SAMM
UF1
UAR
UF1
UAR
UF1
UAR
UF1
UAR
OFF-Apex
0.7196
0.7096
0.8764
0.8681
0.6817
0.6695
0.5409
0.5392
STSTNet Liong et al.
2019
0.7353
0.7605
0.8382
0.8686
0.6801
0.7013
0.6588
0.6810
Graph-AU Leiee
0.7914
0.7933
0.8798
0.8710
0.7192
0.7215
0.7751
0.7890
SLSTT Zhing
272
0.8160
0.7900
0.9010
0.8850
0.7400
0.7200
0.7150
0.6430
FRL-DGT Zhai et al
2023
0.8120
0.8110
0.9190
0.9030
0.7430
0.7490
0.7720
0.7580
SRMCL
BBo
2024
0.8630
0.8830
0.9635
0.9649
0.7946
0.8053
0.8470
0.8866
MFDAN Chi al 2024
0.8453
0.8688
0.9134
0.9326
0.6815
0.7043
0.7871
0.8196
HTNet
Wang
2024
0.8603
0.8475
0.9532
0.9516
0.8049
0.7905
0.8131
0.8124
LTR3O 2hu ot al.
2025
0.8931
0.8819
0.9578
0.9487
0.8336
0.8298
0.8912
0.8526
Ours
0.8943
0.8967
0.9738
0.9754
0.8422
0.8476
0.8716
0.8513
4.2 TRAINING DETAILS
The Transformer for MER consists standard Transformer encoder layers with hidden dimension of 512 and attention heads. The model is trained with the Adam optimizer (Kingma & Ba 2015) on each dataset for 120 epochs, using a learning rate of 2.0 x 10 and a weight decay of 1.0 x 10 5 together with C-VQ-VAE finetuning. The coefficient 5 in Eq. (13) is set to 0.2. The loss weights Anth: Arey Asparse ĀEDLLIP are set to 0.2, 0.1, 1.0 x 10-3, and 0.3, respectively.
4.3 COMPARISON WITH STATE-OF-THE-ARE METHOSS
We first compare method with of-te-of-the- art (SOTA) methods. Table 2 reports the re- sults under the Composite Database Evaluation (CDE) protocol proposed in the MEGC 2019 Challenge (See et al. 2219),, where CASME-II, SMIC-HS, and SAMM are combined to enrich samples for three-class recognition. Our method achieves the highest UF1 score of 0.8943 and UAR of 0.8967 for the overall performance, outperforming other pixel-level motion-based methods. These results highlight the strong ca- pability of the proposed facial action represen- tations extracted from D-FACE. Note that our method achieves the second-hest UF1 and third- best UAR on SAMM, due to the limitation that images in SAMM are grayscale, whereas our model was pretrained on RGB images, which may affect facial action extraction.
Table 3: Comparison with the state-of-the-art meth-
ods CASME-II.
Method
CASME-II (5-class)
ACC
UF1
UAR
Graph-TCN
Ta Ct
2020
0.7246
0.7398
MMNet
2022
0.8835
0.8676
FRL-DGT
Zhu ei
2023
0.7570
0.7480 0.7350
u-bert Nauyen
0.8553 0.8348
SRMCL Bau et al.
2024
0.8320
0.8286
LTR30
0.8178
0.7905
SODA
Zhang 2012
0.8418
0.8141
MER-CLI
2005
0.8233
0.8378
Ours
0.8474
0.8571
0.8694
We further evaluate our method on multi-class scenarios. Table the results on CASME- II for five-class recognition. Despite the lim- ited number of annotated samples, our method achieves competitive performance, ranking sec- ond overall and surpassing other methods based on estimated optical flow. We also report results on the CAS(ME)* dataset, which contains 921 ME clips across four classes. As shown in Ta- ble our method outperforms the second-bel best method, MER-CLIP (Liu et al. 2025), which
Table 4: Comparison with the state-of-the-art meth- ods on CAS(ME)'.
Method
CAS(ME)* (4-class)
UF1
UAR
ne-bett Nguyen et al. 2023
0.4718
0.4913
SFAMNet Liong
2024
0.4462
0,4797
MER-CLIN (Li et
2025
0.6544
0.6242
Ours
0.6807
0.6469
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additionally uses action unit annotations, by 4.02% in UFI and 3.64% in UAR, highlighting robustness in complex multi-class scenarios.
4.4 C-VQ-VAE CAPACITY STUDIES
We conduct capacity studies the codebook size and sequence length of the C-VQ-VA VAE on the CAS(ME)² dataset, as summarized in Table The codebook size specifies the number of feature vec- tors in the codebook, whereas the sequence length denotes the number of facial action tokens that represent the muscle movements between two fa- cial images. The results indicate that the optimal MER performance is achieved with codebook size of 32 and sequence length of 16. Smaller values fail to capture sufficient details of ME mo- tion, whereas larger values lead to redundancy. An overly large codebook destabilizes training and harms MER performance, where many fea- ture vectors the codebook become infrequently used or capture unnecessary details rather than essential facial motions, resulting in less meaning- ful representations. This suggests that compact yet expressive tokenization is crucial for capturing subtle ME motion without overwhelming the subsequent Transformer with noisy or redundant information.
Table 5: Capacity studies on the codebook size and sequence length of the C-VQ-VAE.
BookSize
SeqLen
CAS(ME) (4-class)
UFI
UAR
0.5210
0.5217
16
16
0.6726
0.6378
32
16
0.6807
0.6469
64
16
0.6227
0.5963
128
16
0.6399
0,6017
25
0,6481
0.6177
32
25
0.6578
0,6320
64
25
0.6423
0.6071
Table 6: Ablation studies on the choice of recognition network, feature aggregation strategy, and EDCLIP loss on CASME-II for five-class classification.
Category
Method
Acc
UFI
UAR
Recognition Network
CNN Backbone
0.8313
0.8120
0.8204
Transformer with 2D PE
0.8327
0.8164
0.8234
Transformer with ID PE (ours)
0.8474
0.8571
0.8694
Feature Aggregation
Average Pooling
0.8434
0.8345
0.8374
Class Token
0.8313
0.8294
0.8593
Sparse Attention Pooling (ours)
0.8474
0.8571
0.8694
EDCLIP Loss
w/o EDCLIP
0.8394
0.8286
0.8384
w/ EDCLIP (ours)
0.8474
0.8571
0.8694
4.5
ABLATION STUDIES
We conduct ablation studies to validate the effectiveness of each component in our framework. The results are shown in Table
Recognition Network. To verify the correctness of our empirical observations on facial action tokens, we replace Transformer with positional embeddings (PE) by either a CNN backbone or Transformer with 2D PE, while keeping the same number of network layers. The results show that the Transformer with ID PE outperforms both alternatives, demonstrating the advantage of ID sequential modeling over 2D spatial structures in capturing order-dependent token semantics.
Sparse Attention Pooling. Next, we examine the contribution of sparse attention pooling by comparing it with two alternatives: average pooling and the class-token mechanism. The results show that sparse attention pooling yields better performance, confirming that only a subset action tokens is informative and should be emphasized for MER.
EDCLIP Loss. Finally, we evaluate the effectiveness of the EDCLIP loss. We compare models trained with and without the EDCLIP loss. Results show that incorporating emotion descriptions

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Surprise
Disgust
[yybrro
(Lid Tightaner
+ Eyelid Raiser)
Eyebrow Lower)
10
1
25
X
11
20
27
5
20
27
16
16
X
X
21
28
30
31
21
12
25
20
dcca Action Tokens with Attertion
accal Action Tokeee with Altention
Irput
Input
Generated Generated
Generated ME
Generated
Figure 4: Visualization of sparse attention pooling and facial action tokens. For each sample, the action information between onset and apex frames encoded into action tokens, weighted by sparse attention pooling (darker indicates higher weight). The salient tokens are then applied to cross-identity and tross-domane faces, generating new facial images that preserve the same underlying MEs.
further improves both accuracy and balanced metrics, showing that bridging action tokens to emotion semantics provides additional supervision and enhances the discriminability learned features.
4.6 QUALITATIVE ANALYSES
In addition to quantitative results, we provide qualitative analysis to demonstrate the effectiveness of sparse attention pooling and the generalization of our facial action tokens. The visualizations are presented in Figure 4 For a given ME, we first visualize the index map of facial action tokens with attention scores, which highlight the critical tokens for recognition. To further examine their validity and generalization ability, we perform controlled manipulation experiments by applying the salient action tokens 1) an image from different subject; and 2) an in-the- face collected from unconstrained conditions. For each sample, we first use two identical facial images to determine the index map corresponding to the "no action" state. Then, selectively replace only the indices emphasized by sparse attention pooling with those from a real ME pair, while keeping the rest unchanged. The reconstructed results consistently preserve the underlying identity while presenting the intended expression, validating both the effectiveness of sparse attention pooling and the cross- subject, cross-domain generalization ability of our learned facial action tokens.
In addition, we analyze the facial action tokens in the learned codebook and observe several interesting findings. Specifically, tokens with higher similarity the feature space tend correspond similar motion semantics, while variations in feature distance are related to differences in expression intensity, Based on this insight, we further investigate: 1) diverse ME generation with various intensities, and 2) the interpretability of the codebook for controllable expression generation. More detailed analyses and visualizations are provided Appendices A. and A.2
5 CONCLUSION
In this paper, we introduced D-FACE, a Discrete Facial ACtion Encoding framework pretrained on large-scale facial video data, to discretize facial muscle movements between two facial images into identity- and domain-invariant facial action tokens. Through empirical analyses, we observed that these action tokens exhibit order-dependent semantics and can be organized as one-dimensional sequences, which motivated the use of a Transformer with sparse attention pooling to capture informative local cues for MER. Furthermore, we incorporated an emotion -eneriionddeerpttioon-.o guided CLIP (EDCLIP) alignment to explicitly bridge action tokens with EDCLIP, the "others" category, which lacks appropriate textual prompts due to its ambiguity, is pushed away from other textual embeddings. Extensive experiments on multiple MER datasets demonstrated that D-FACE not only achieves state-of-the of-the-art performance but also exhibits strong cross-subject and cross-domain generalization, including the ability for ME generation. These results suggested that shifting from pixel-level motion descriptors to robust semantic-leww action tokens offers promising perspective for ME analysis.
emotions. In
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ACKNOWLEDGMENTS
This work was supported by Innovation Platform for Society 5.0 from Japan Ministry of Education, Culture, Sports, Science and Technology, and JSPS KAKENHI Grant Number JP24K03010.
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A APPENDIX
A.1 DIVERSE
GENERATION
We further investigate the generation of diverse MEs with various intensities. To this end, we analyze the feature similarity and distance among the vectors in the codebook, as illustrated in Figures and The results indicate that certain feature vectors exhibit high similarity and low distance, suggesting they convey highly similar semantics. We hypothesize that tokens with high similarity share the same facial motion semanties, while feature distance regulates expression intensity. Quantitative experiments conducted to verify this hypothesis, with results shown in Figure These results demonstrate that diverse ME generation with various intensities can be achieved by replacing a specific facial action token with another highly similar token.
Similarity Matrix
18
9.79
025
06.
040
5:0
70
25
617
A2-AAA A2-4.10 401---1-- 40----48-------0000- -.3---4- 0.30--3---0 0212--1--00 2255--55---- 4M
4030
8.5
100
855
OUT
-33
14.22
0.20
85
1100
0.1
8.87
58 837
0.9
075
020
002
Figure Cosine similarity among the feature vectors in the codebook.
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Feature Distance Matrix
0024
2214.0 444000000
687
KAC
139
0459
20111221
1:0
1.43
2316622
12.100
1511
14.3
16.55
6:
9.00
A1
Figure Euclidean distance among the feature vectors in the codebook.
Neutral
Neutral Face
20
Surprise (Opening Mouth)
Disgust (Eybrro Lower!
InputME
Generated ME mL
Generazed ME #21
Input ME
Generated ML mt..
Generated ME #2
Figure 7: Visualization of intensity control in ME generation. For each sample, we first extract facial action tokens from an ME clip. These tokens are then applied to a neutral face to generate facial image that presents the same ME. By selectively replacing certain action tokens with highly similar alternatives, we generate a new facial image that preserves the original ME while exhibiting different intensity.
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A.2 INTERPRETABILITY OF THE FACIAL ACTION TOKENS
In this subsection, we further investigate the interpretability of the learned facial action tokens. Using the similarity matrix (Figure 5) and the feature distance matrix (Figure 6). we cluster the 32 codebook vectors into groups with consistent semantics, as summarized in Table 7 To examine the motion semantics of each token, we perform systematic 6 x 16 manipulation study, where each cluster is substituted every sequence location of the index map. Specifically, we first generate "no action" index map from two identical facial images. Then, by altering one cluster at time while keeping all others fixed, we observe the resulting facial image. The summarized observations are presented in Table g
Table 7: Clustering results the codebook feature vectors.
Cluster
Indices
0
1, 6, 9, 11, 30
5, 12, 15, 16, 26, 27,
2
0, 14, 17
3
18, 22, 23, 28, 29
4
13, 19, 20, 21, 24, 25
5
4, 4,7,8, 10
Table 8: The emotion-related motion semantics of each token at each sequence location of the index map. The motions are described approximately in terms of Action Units (AUs) (Ekman Rosenberg 1997). "Other AUs" indicates AUs not directly involved in MEs (e.g., eyeball movements, head movements).
Clus.
3
4
5
Pos.
(0, 0)
Other AUs
Other AUS
Other AUs
Other AUs
Other AUs
No action
(0, 1)
No action
Other AUs
Other AUs
Other AUs
Other AUs
Other AUs
Slight 4+7
Marked
Strong
Slight
Trace
(0, 2)
No action
4+7
4+7
4+7
4+7
(0, 3)
No action
Other AUs
Other AUs
Other AUs
Other AUs
Other AUs
Slight
Trace
Strong
Marked
Slight
(1, 0)
No action
1+2+12
1+2+12
1+2+12
1+2+12
1+2+12
(1, 1)
Other AUs
Other AUs
Other AUs
No action
Other AUs
Other AUs
(1,
Other AUs
Other AUs
Other AUs
No action
Other AUs
Other AUs
Slight
Trace
Strong
Marked
Slight
(1, 3)
No action
12
12
12
12
12
Trace
Trace
Marked
Marked
Slight
(2, 0)
No action
5+12
7+23
5+12
7+23
5+12
Slight
Strong 4+7
Trace
Trace
Marked
(2, 1)
No action
4+7
1+2+5
4+7
4+7
Slight 10
Strong
Trace
Trace
Marked
(2, 2)
No action
10
24
10
10
Slight
Strong
Trace
Trace
Marked
(2, 3)
No action
24
10
24
24
Slight
Slight
Strong
Strong
Marked 7
(3, 0)
No action
7
5
Slight
Slight
Strong
Strong 24
Marked
(3, 1)
No action
25
24
25
25
Slight 7+24
Slight
Strong 7+24
Strong
Marked
(3, 2)
No action
5+25
5+25
7+24
Slight 7+26
Slight 5+17
Strong 7+26
Strong
Marked
(3. 3)
No action
5+17
7+26
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Eyebrow lower Eyelid Lightener
5
0
3
Neutral
Eyebrow lower + Lips apart
5
0
3
3
Eyelid Lips apart
4
5
0
3
3
4
5
0
3
3
4
1
4
Eyelid tightener drop
3
3
3
4
1
1
2
4
4
2
Neutral
3
Eye closing drop
4
2
4
4
Eyelid tightener
Figure 8: Visualization of controllable expression generation. For each sample, we show the neutral face (left), two generated facial expressions with single token replaced (middle), and the composite facial expression (right).
We conduct qualitative experiments to validate observations through controllable expression generation. Some representative results are shown in Figure & indicating that different facial action tokens can be flexibly combined to generate diverse facial expressions. These results further support that token interactions rely on order-dependent and global contextual modeling rather than 2D dependent structure.
A.3 SENSITIVITY ANALYSIS OF TRANSFORMER HYPERPARAMETERS
To evaluate the stability of the Transformer for MER, we conducted a sensitivity analysis on two key architectural hyperparameters: the number of attention layers Latto and the hidden dimension- ality d. Specifically,
Table 9: Ablation study of Transformer depth Laun and hidden size d under the CDE protocol.
Lattn
d
Composite
evaluated models with (1, 2, 3, 4) layers and hidden dimensionality {256, 512, 768. 1024] while keeping all other settings unchanged.
UFI
UAR
512
0.8789
0.8927
2
512
0.8943
0.8967
As shown in Table 9 the overall performance on the composite dataset under the CDE protocol dataset show that the overall performance varies only slightly across different settings, and all con- figurations achieve comparable accuracy. The model with 2 layers and hidden size of 512 of- fers a good trade-off between efficiency and per- formance, which we therefore adopt in the main experiments. These observations indicate that
512
0.8905
0.9032
512
0.8816
0.8967
256
0.8764
0.8877
512
0.8943
0.8967
768
0.8798
0.8990
2
1024
0.8717
0.8734
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results insensitive to reasonable hyperparameter changes. These results also demonstrate that framework is a stable and reprodueible pipeline.
A.4 EXAMPLE SAMPLES FROM THE PRETRAINING DATASET
In this subsection, we visualize several examples of frame pairs obtained through our sampling strategy from the large-scale facial video dataset VoxCeleh, as shown in Figure These examples provide an intuitive illustration of the diversity present in our pretraining data, including variations in identity, expression intensity, and recording conditions. Such diversity is essential for enabling the C-VQ-VAE codebook to learn robust and generalizable semantic motion representations.
Figure 9: Example frame pairs sampled from the VoxCeleb pretraining dataset.
A.5 ABLATION STUDY ON THE CHOICE OF TEXT PROMPTS IN EDCLIP.
To validate the design of our EDCLIP, con- duct an ablation study on the choices of text prompts used for aligning action tokens with emo- tion semantics. We compare our default prompt, "A [emotion] expression with [general facial ac- tion descriptions).., against two alternative tem- plates: 1) "A [emotion] face showing [general fa- cial action descriptions)."; and 2) "A [emotion] expression with [AU annotations of each sam- ple].", where the general action descriptions replaced with AU-based descriptions following MER-CLIP (Liu al. 2025). For the AU-based setting, samples belonging to the same emotion category may correspond different AU combi- nations and thus different textual prompts.
Table 10: Ablation study on the text prompts used in EDCLIP. Variant #1 corresponds to "A [emotion] face showing [general facial action descriptionss.." while Variant #2 corresponds to "A [emotion] expression with [AU annotations of each sample].". where the AU descriptions follow MER-CLIP (Lu et al.
2025)
Template
CASME-II (5-class)
ACC
UF1
UAR
Variant #1
0.8514
0.8565
0.8617
Variant #2
0,8273
0.8255
0.8231
Ours
0.8474
0.8571
0.8694
The results are shown in Table IO] We observe
that the imneral-action-baseeee variant yields performance close to our default template, indicating that EDCLIP is not overly sensitive to minor template changes and remains stable and effective. In contrast, the AU-based prompts lead to performance degradation. The reason is that AU-based descriptions vary across samples even within the same emotion class. Consequently, each emotion category no longer shares consistent textual embedding but instead corresponds to many distinct AU-based textual vectors. This removes the class-level semantic anchor in the text embedding space and forces samples of the same emotion to be aligned toward different textual directions. Such misalignment weakens the contrastive learning objective, destabilizes training, and ultimately harms recognition performance in framework.
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In contrast, original design assigns one textual prompt to each emotion class by combining the emotion name with general facial action descriptions. This ensures that all samples of the same emotion are pulled toward single, stable semantic embedding, providing strong and consistent anchor for alignment. The ablation results further support that our facial action tokenizer already learns robust and discriminative motion representations without requiring precise AU annotations.
A.6 QUALITATIVE EVALUATION ON IN-THE- WILD AND
FACES
To evaluate the cross-domain generalization ability of D-FACE, we conduct qualitative experiments on collection of in- will facial images obtained from unconstrained online sources. For each facial image, we first generate the token map that corresponds to the "no-action" state, and then selectively alter token indices generate new facial images.
To further evaluate robustness under low-quality inputs, we downsample each facial image to 20% of its original resolution and then feed the degraded images into the facial action tokenizer. We repeat the same procedure: obtaining the "no-action" token map and applying the same token manipulations for facial image generation.
As shown in Figure TO D-FACE generalizes well on both high-resolution and low-quality in-the-wild wild images, generating natural faces with diverse facial actions. These results demonstrate that the learned facial action tokens encode stable and meaningful motion semantics that transfer reliably across identities, domains, and even under substantial resolution degradation.
Reference Face
Roltrence Face
11
11
2
13
24
High-Rasolltion bnthh-willd Image
20
19
(a) Remais Unchanged
[b] Lid Tightaner Lip
[c] Head Turn Left
(e) Remain Unchanged
Ibi Upper Lid Raiser Slight
Tightener
Refleoncc
20
22
23
29
22
29
54
29
73
22
Low Res-Resllution In the wild Image
Unchanged
Lip Pressor
Lipps part
Unchanged
Slight Comer Pulter
Lid Tightener
Qualitative evaluation high- and low-quality in-the-will wild facial images. The numbers in red stand for the altered indices.
Figure 10:
A.7 EVALUATION ON MICRO-EXPRESSION EXPRESSION ACTION UNIT RECOGNITION
To further evaluate the robustness of the learned facial action tokens, extend our framework beyond MER and conduct ME AU recognition, which provides a direct evaluation the quality of the motion representations. Following the evaluation protocol of et al. (2021), we perform AU recognition on the CASME-II dataset and report the Fl-scores for AU1, AU2, AU4, AU7, AU12, AU14, AU15, and AU17, 7, as well as the average F1-score. In this experiment,
the same pretrained facial
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Table 11: Evaluation micro-expression action unit recognition on the CASME-II dataset.
Method
AU1
AU2
AU4
AU7
AU12
AU14
AU15
AU17
AVG
Li ct al. 22021
0.726
0.721
0.898
0.569
0.796
0.685
0.715
0.700
0.726
Ours
0.902
0.868
0.849
0.660
0.667
0.636
0.828
0.800
0.776
action tokenizer and the same Transformer architecture with identical hyperparameter settings, while removing the EDCLIP module since AU recognition does not require aligning motion representations with textual emotion semantics.
As shown in Table our framework achieves average Fl-score of 0.776 CASME-II, even without task-specific considerations and module designs. We compare our results with the most recent work published in established venues. Despite not being designed for AU recognition, the proposed semantic- facial action tokens remain robust and effective, demonstrating its transferability across tasks.
A.8 ABLATION STUDY ON Loss WEIGHTINGG
We also conducted ablation studies the loss weights of the EDCLIP loss (i.e., AFnCL.P) and the margin-based constraint for the "others" category (i.e.. Aoch) to determine the optimal hyperparam- eters. The results are shown in Table [2] For ĀEDCLIP. we find that a small weight leads to in- sufficient contrastive alignment, while an overly large weight affects the classification objective. For Aoth. we observe that overly large values put too much emphasis separating the "others" sam- ples from all textual embeddings, thus leading to performance degradation. Based on these obser- vations, we set λEDCLI = 0.3 and Aath = 0.2 for all experiments.
Table 12: Ablation study on loss weighting.
CASME-II (5-class)
AEDLLIP
Acc
UFI
UAR
0.0
0.8394
0.8286
0.8384
0.1
0,8434
0.8409
0.8604
0.3
0.8474
0.8571
0.8694
0.5
0.8474
0.8455
0.8631
0.7
0,8394
0.8353
0.8429
Aoth
Acc
UFI
UAR
0.0
0.8474
0.8472
0.8643
0.2
0,8474
0.8571
0.8694
0.4
0.8514
0.8356
0.8533
0.6
0.8434
0.8365
0.8438
A.9 EFFECT OF THE DISCRETIZATION BOTTLENECK IN VQ-VAE
To examine whether the discretization bottleneck in VQ-VAE loses fine-grained motion nuances crucial for MER, we conduct an additional experi- ment using the same pretrained C-VQ-VAE. As in our main framework, we first compute the feature difference between the two input frames, but remove the quantization step and keep the motion features in continuous latent space.
Table 13: Evaluation of the discretization process in VQ-VAE on the composite dataset under the CDE protocol.
Method
Composite
UFI
UAR
w/o discretization
0.8762
0.8799
w/ discretization (Ours)
0.8943
0.8967
As shown in Table 13
removing discretization
leads a clear drop MER performance, even though discretization may theoretically introduce some loss of reconstruction details. This result confirms that the quantized representations are cleaner and more motion-focused, effectively filtering out irrelevant appearance variations, which is beneficial for MER.
A.
THE USE OF LARGE LANGUAGE MODELS (LLMs)
used LLMs for grammar checking.
20


Reviews:
[{"review_id": "kMPemHuOlY", "reviewer": "Reviewer_MjG1", "summary": "This paper proposes D-FACE, a novel framework for micro-expression recognition (MER) that transitions from pixel-level motion descriptors (e.g., optical flow, frame difference) to semantic-level discrete facial action tokens. The approach employs a conditional VQ-VAE to encode facial motion between onset and apex frames into discrete tokens, modeled as sequences with a Transformer using sparse attention pooling. Moreover, an emotion-description-guided CLIP (EDCLIP) module aligns visual features with textual emotion semantics, improving interpretability and generalization. Extensive experiments on CASME-II, SMIC-HS, SAMM, and CAS(ME)3 demonstrate state-of-the-art performance and superior cross-domain generalization.", "questions": "What is the scale and composition of the “large-scale facial video data” used for D-FACE pretraining?\n\nHow does the choice of codebook size (K=32) affect semantic granularity? Could larger K provide richer action semantics, or would that harm stability?\n\nDid you explore temporal modeling beyond onset–apex pairs (e.g., full ME sequences)?\n\nHow sensitive is EDCLIP performance to the choice of emotion text prompts?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper convincingly reframes MER from a low-level motion estimation problem to a semantic-level action representation problem — an important conceptual contribution.\n\n2. The architecture integrates C-VQ-VAE for discrete motion tokenization, Transformer-based sequential modeling, and EDCLIP for semantic alignment. Each component is well-motivated and supported by ablation studies.\n\n3. Empirical thoroughness: Results on multiple MER benchmarks (CASME-II, SMIC-HS, SAMM, CAS(ME)3) are comprehensive. Ablation studies (Table 6) validate each design choice (Transformer, sparse pooling, CLIP alignment). Cross-identity and cross-domain generation experiments (Fig. 4) provide strong qualitative evidence of generalization.\n\n4. The codebook visualization and token analysis (Appendix A.1–A.2) demonstrate that the learned discrete tokens correspond to interpretable, position-dependent facial actions — a rare property in MER models.", "weaknesses": "1. While empirically effective, the paper could strengthen its argument about why discretization (VQ-VAE) leads to domain invariance — possibly through statistical or theoretical analysis of identity disentanglement.\n\n2. The large-scale pretraining dataset for the facial video tokenizer is only mentioned briefly. It’s unclear: Was identity information explicitly removed or balanced? How was pretraining validated before fine-tuning on MER datasets?\n\n3. The contrastive objective (Eq. 12–13) is well-motivated, but comparisons with existing CLIP-based MER methods (e.g., MER-CLIP, 2025) could include a clearer ablation or embedding visualization to show how EDCLIP differs in practice.\n\n4. The authors claim D-FACE enables generalizable facial action understanding, but experiments are limited to MER datasets. It would strengthen the paper to evaluate transferability to macro-expression or AU-based tasks.\n\n5. On the SAMM dataset, performance drops due to grayscale input. The authors acknowledge this but could have mitigated it with color-agnostic pretraining or domain adaptation.", "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": "ZPLTNgKpbH", "reviewer": "Reviewer_mHMN", "summary": "This paper presents D-FACE, a novel framework for micro-expression recognition (MER). The work's primary contribution is a shift away from traditional pixel-level motion representations (like optical flow), which are sensitive to identity, toward a learned, discrete, semantic-level representation called \"facial action tokens.\" The method uses a C-VQ-VAE pretrained on large-scale video data to \"tokenize\" facial movements. It then models these tokens as a 1D sequence using a Transformer with sparse attention, motivated by an empirical analysis of token semantics. Finally, it aligns these tokens with human-understandable emotions using a novel CLIP-based loss (EDCLIP), which includes a specific margin-based objective to handle the ambiguous \"others\" category. The method achieves state-of-the-art results and, more importantly, demonstrates impressive cross-subject and cross-domain generalization in qualitative generation experiments.", "questions": "What are the current inference costs, and are there any optimization options?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The central idea of discretizing facial motion into semantic tokens is a well-motivated and significant departure from standard pixel-based MER methods. The authors clearly identify the key weaknesses of prior work (identity sensitivity and lack of semantics) and propose a direct solution.\n2. The cross-identity and cross-domain generation experiments in Figure 4 are powerful evidence for the paper's main claim. The ability to transfer an ME from one person to another by only transferring the learned tokens, while preserving the target identity, convincingly demonstrates that the D-FACE representation is successfully disentangled from identity.\n3. The framework is well-designed and internally consistent. The empirical analysis in Section 3.2, which finds that tokens behave like 1D sequences, provides a clear justification for using a Transformer (a choice validated in the ablations). The sparse attention mechanism is well-suited for the local nature of MEs, and the EDCLIP module's specific handling of the \"others\" category is an intelligent solution to a practical problem in emotion recognition datasets.", "weaknesses": "1. The leap from the empirical analysis (Section 3.2) to the 1D Transformer modeling is not fully convincing. The paper states that \"token semantics are position-dependent\" (e.g., token '2' means something different at position (3,3) than at (1,0) in Figure 3). However, it also claims the \"absolute 2D spatial positions... do not correspond to specific locations\" and that this motivates flattening to a 1D sequence. This seems contradictory. The observation that meaning is position-dependent could equally (or perhaps more strongly) motivate the use of a 2D Transformer with 2D positional embeddings. The paper would be stronger if it provided a clearer explanation for why a 1D sequence is the correct interpretation of this finding.\n2. The C-VQ-VAE tokenizer is pretrained on VoxCeleb, a dataset of unconstrained *macro-expressions* (e.g., people talking in interviews). The model is then fine-tuned and evaluated on *micro-expressions*, which are defined by their subtle, rapid, and involuntary nature. This is a significant domain gap. The authors do not discuss whether pretraining on large, voluntary motions biases the codebook and prevents it from learning the extremely subtle motions unique to MEs.\n3. The paper states it adapts a C-VQ-VAE architecture from a robotics paper [1] and \"refined the codebook design and latent action length\". However, the specific details of these refinements are not provided. This makes the core tokenizer component, which is responsible for the paper's main contribution, difficult to understand and reproduce. And more related works should be discussed to support the adaptation. [2]\n4. The action encoder architecture in Figure 2(b) includes a \"Causal Transformer\" that processes features from the two input frames. The term \"causal\" implies temporal masking, but it is not explained how this is applied between just two static frames ($I_1$ and $I_2$). This component's function and design are unclear.\n\n[1] Seonghyeon Ye, et al. Latent action pretraining from videos. Proceedings of the International Conference on Learning Representations, 2025.\n\n[2] Chen, Haodong, et al. \"Finecliper: Multi-modal fine-grained clip for dynamic facial expression recognition with adapters.\" Proceedings of the 32nd ACM International Conference on Multimedia. 2024.", "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": "ugAGw5aIUl", "reviewer": "Reviewer_hr6P", "summary": "The authors are the first to employ semantic-level facial action tokens for MER and model these tokens as 1D sequences analogous to human language. Furthermore, they design an alignment method to establish explicit connections between action tokens and human-understandable emotions. Experiments demonstrate state-of-the-art accuracy and strong cross-subject and cross-domain generalization.", "questions": "- The experimental section lacks an ablation study on the weighting of loss components. Would exploring different combinations of loss weights further improve the performance?\n\n- The authors mention that the sub-optimal performance on SAMM is due to training on RGB images. Would training on grayscale images reduce the model’s reliance on color and improve performance?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The authors introduce a new perspective in the MER field: they argue that previous works operate in the pixel domain, making them highly sensitive to identity variations, and thus shift the focus toward a more robust feature domain.\n\n- The authors experimentally demonstrate why 1D token sequences better represent expressions than 2D token maps with absolute positional information, thereby reinforcing the rationality of their design.\n\n- Their ablation studies on the core components clearly verify the independent contributions of each module, including the Transformer, sparse pooling, and EDCLIP loss.", "weaknesses": "- Lacks ablation comparisons with different combinations of loss function weights.\n\n- Shows sub-optimal performance on grayscale datasets.", "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": "QxBL4IjX55", "reviewer": "Reviewer_ww2V", "summary": "This work is about micro expression recognition. MER is very useful for many applications yet very difficult to detect. This work set the task in a semantic framework, instead of relying on pixel-level MER, as most existing work did. A Transformer was used to process flattened latent feature vector, followed by linear projection and alignment to identify micro expression. Results were good and ablation study was supportive of the necessity of each step.", "questions": "Please see weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Strengths include the approach to shift from pixel-wise MER to semantics-based MER, and supportive ablation study.\nIt sounds like a reasonable idea to discretize the difference between two consecutive frames into identity- and domain-invariant action representations, but the ensuing description of the method did not seem to pursue this idea.", "weaknesses": "The writing is not always easy to follow. It is not clear why authors performed face generation by inserting facial token into various locations at the action token matrix. What was the purpose of this step? Is it just to demonstrate that locations of the same token leads to different facial expression? \nIn Eq. 5, what was the linear projection P? Can the authors give the detail? \nA big concern is the impact of the size of codebook on the final performance, Table 5. It is puzzling that the size of the codebook would have such a big effect on the capacity, and it is worrisome that a larger codebook size actually led to reduced capacity, according to the table.\nIn the method, which part is the identity-invariant action representation and which part is the domain-invariant action representation? It seems these two representation were not described in the algorithm.", "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": "wIOJhg4r1i", "reviewer": "Reviewer_UyN7", "summary": "The paper introduces D-FACE, a Discrete Facial ACtion Encoding framework that shifts micro-expression recognition (MER) from pixel-level motion descriptors (e.g., optical flow) to semantic-level facial action tokens. Using a conditional vector-quantized VAE, D-FACE discretizes facial muscle movements into domain- and identity-invariant “action tokens.” These tokens are treated as one-dimensional sequences and processed with a Transformer featuring sparse attention pooling to capture localized, discriminative cues. An additional emotion-description-guided CLIP (EDCLIP) alignment connects visual token features with textual emotion descriptions, enhancing semantic interpretability. Experiments on several benchmark datasets show that D-FACE achieves competitive or superior accuracy and exhibits cross-subject and cross-domain generalization.", "questions": "In addition to the questions relevant to weaknesses, there are a few more:\n(1) How generalizable is D-FACE to in-the-wild, low-quality videos, given its dependence on large-scale pretraining on high-quality data?\n\n(2) Could the discretization bottleneck of VQ-VAE lose fine-grained motion nuances crucial for micro-expression detection?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Proposes a novel paradigm shift for MER: from pixel-level motion descriptors to semantic-level, discrete facial action tokens, potentially improving robustness and transferability.\n\n2. Designs a C-VQ-VAE–based facial action tokenizer, the first to discretize motion representations for micro-expressions. Introduces a Transformer with sparse attention pooling to focus on informative local cues, demonstrating improved interpretability and performance.\n\n3. Develops EDCLIP alignment to connect visual action tokens with human-understandable emotion semantics, enhancing explainability.\n\n4. Shows cross-domain and cross-identity generalization, as well as potential for facial expression generation, marking a broader contribution beyond recognition.", "weaknesses": "This paper has a few issues:\n(1) Figure 1(b) (and related illustrations) are difficult to follow, limiting accessibility of the proposed conceptual shift, especially the interpretation of token map. A few more descriptive words would help. \n\n(2) The novelty, while conceptually interesting, relies heavily on adapting existing VQ-VAE and CLIP mechanisms. In the introduction, it is better to further clarify how the method contributes novel insights on top of existing mechanisms. \n\n(3) Logic clarity in parts (especially empirical analysis of tokens in Sec. 3.2) could be improved; the link between position-dependent semantics and 1D modeling is asserted but not quantitatively validated. Method correctness appears sound but somewhat over-engineered; e.g., the choice of hyperparameters and training stability of the C-VQ-VAE + Transformer pipeline is not discussed in detail.\n\n(4) The reported accuracy improvements are marginal (e.g., small gains over LTR-3O and SRMCL in Table 2), raising questions about practical significance. There is little ablation or visualization proving that the semantic tokens truly capture meaningful, interpretable muscle movements.", "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)