3DPE-Gaze:Unlocking the Potential of 3D Facial Priors for Generalized Gaze Estimation
Authors: Yangshi Ge
Yiwei Bao
Feng Lu
Venue: NeurIPS 2025 poster ()
Keywords: Gaze Estimation
Cross-Domain Adaptation
3D Facial Priors
Primary area: deep_learning

Abstract: In recent years, face-based deep-learning gaze estimation methods have achieved significant advancements. However, while face images provide supplementary information beneficial for gaze inference, the substantial extraneous information they contain also increases the risk of overfitting during model training and compromises generalization capability. To alleviate this problem, we propose the 3DPE-Gaze framework, explicitly modeling 3D facial priors for feature decoupling and generalized gaze estimation. The 3DPE-Gaze framework consists of two core modules: the 3D Geometric Prior Module (3DGP) incorporating the FLAME model to parameterize facial structures and gaze-irrelevant facial appearances while extracting gaze features; the Semantic Concept Alignment Module (SCAM) separates gaze-related and unrelated concepts through CLIP-guided contrastive learning. Finally, the 3DPE-Gaze framework combines 3D facial landmark as prior for generalized gaze estimation. Experimental results show that 3DPE-Gaze outperforms existing state-of-the-art methods on four major cross-domain tasks, with particularly outstanding performance in challenging scenarios such as lighting variations, extreme head poses, and glasses occlusion.

3DPE-Gaze:Unlocking the Potential of 3D Facial Priors for Generalized Gaze Estimation
Yangshi Ge
Yiwei Bao Feng Lu"
State Key Laboratory of VR Technology and Systems, School of CSE, Beihang University {geyangshi, baoyiwei, lufeng) edu.
Abstract
recent years, face-based deep-learning gaze estimation methods have achieved significant advancements. However, while face images provide supplementary information beneficial for gaze inference, the substantial extraneous information they contain also increases the risk of overfitting during model training and com- promises generalization capability. To alleviate this problem, we propose the 3DPE-Gaze framework, explicitly modeling 3D facial priors for feature decoupling and generalized gaze estimation. The 3DPE-Gaze framework consists of two core modules: the 3D Geometric Prior Module (3DGP) incorporating the FLAME model to parameterize facial structures and gaze-irrelevant facial appearances while extracting gaze features; the Semantic Concept Alignment Module (SCAM) separates gaze-related and unrelated concepts through CLIP-guided contrastive learning. Finally, the 3DPE-Gaze framework combines 3D facial landmark prior for generalized gaze estimation. Experimental results show that 3DPE-Gaze outperforms existing state-of-the-a methods on four major cross-domain tasks, with particularly outstanding performance in challenging scenarios such as lighting variations, extreme head poses, and glasses occlusion.
Introduction
Gaze estimation has wide applications in computer vision, being essential technology in sce-
narios such as human-computer interaction 13 28 29]. virtual/augmented reality [25] [31, and
driving monitoring I1 19 27 Gaze estimation methods can be broadly categorized into two types: model-based approaches and appearance-based approaches. Model-based approaches calculate gaze direction by simulating the anatomical structure of the eyeball, offering higher accuracy but typically requiring specialized hardware and controlled environments. Appearance-baan based approaches, on the other hand, learn gaze mapping relationships directly from image features, offering broader application potential.
Appearance-basee gaze estimation research has undergone critical transition from focusing solely on eye regions to utilizing full-face information. Early research primarily extracted local features from eye regions 26 220), but this approach overlooked the important contextual information provided by other facial areas. Zhang et al. (38) pioneered the use of full-face input and introduced a spatial weighting mechanism, significantly improving prediction accuracy. Since then, full-face input has become the mainstream approach gaze estimation research.
However, the full-face input strategy is double-edged sword: while it contains rich gaze-related structural information, the eye region occupies only a small proportion of the image, simultancously introducing numerous domain-specifie interferences (such as lighting variations, skin color differ- ences, expression changes, etc.) 33) These irrelevant information easily lead to models overfitting to
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Feature contusion
Error
Estimation Error
Traditional
Sourse Domain
Mixed Features
Target Domain
10.34
Endr Incnass
7.40
Raconder
Domain Noien
Face Structure
Regresser
MI Si-HHeilSSeeee Heal
Rotution
Putaaioo
Saasdad Lureaaiie
Powere Bairston
lacerporating
Facial Prñors
Estimation Error
Geumetric Prior Module
Semaatic Concept Aligameet Medile
3D Priors Doost Generalization
10
Eroor
13.00
CLIP Contrant
FLAME
Sepersic
660
Attentian
Prediction
With
Withour
Coleee
Mixed Features
Face Modeling
Separated Features
30 Priurs
Generalize
Figure 1: Left shows traditional CNN exhibiting clear performance collapse under standard conditions versus domain shift conditions. Right briefly demonstrates how we introduce 3D facial priors.
appearance features in the training data, severely affecting cross-domain generalization capability. To address this challenge, researchers have proposed various solutions, such adversarial learning[Z]. data perturbation [33], feature separation 18), and multimodal [usion[30] 35). However, most of these methods remain at the image feature level and fail to fully utilize 3D facial priors, making it difficult for them to capture the inherent geometric structure of the face and more susceptible to surface appearance changes. Therefore, how to fully utilize structured facial information while effectively filtering domain-specing noise remains a key challenge in the field of gaze estimation.
To address this challenge, propose the Threee-Dimesiional Prior Enhanced Gaze Estimation framework (3DPE-Gaze). consisting of two complementary core modules: 3D Geometric Prior Module (3DGP) and Semantic Concept Alignment Module (SCAM). As shown in Figure I the 3DGP module decouples head pose, expression, shape, and gaze parameters from 2D facial images through the FLAME parametric face model, transforming gaze estimation from prediction process based on domain- susceptible 2D appearance features to one based on stable geometric structures. However, FLAME- encoders [9] have inherent limitations in precisely modeling eyeball rotation and capturing subtle eye details, making it difficult to fully express complex gaze behaviors. To compensate for this deficiency, we designed the SCAM module as a complement. This module, based on CLIP semantic representations, distinguishes between gaze-relevant and irrelevant features through contrastive learning- Our FacePrior-Gare Predictor effectively fuses these two complementary types of information, enabling the model to simultancously leverage structured geometric constraints and high-level semantic concepts, thereby enhancing cross-domain generalization capability.
In summary, 3DPE-Gaze systematically injects 3D facial priors and semantic concept priors into the gaze estimation process, effectively solving the cross-domain generalization problem in gaze prediction. Our experiments on multiple benchmark datasets have validated the excellent cross- domain performance of this method, achieving significant performance improvements various challenging scenarios compared to current ofate-of-the-art methods. The main contributions of this research are as follows:
We propose the 3D Geometric Prior Module (3DGP), novel parametric encoder that transforms the gaze estimation problem from pixel space to parameters such as head pose, expression, and shape, establishing the foundation for associating gaze prediction with 3D facial structure and achieving effective extraction of comain--nvariiae features.
We propose the Semantic Concept Aligmment Module (SCAM), specifically addressing the limitations of pure geometric modeling in capturing complex gaze semanties. This module innovatively leverages CLIP pre-trained knowledge and contrastive learning strategies to explicitly distinguish gaze-relevant concepts from domain interference factors in the feature space, allowing the model to adapt to new environments without any target domain data.
Experimental results show that our 3DPE-Gaze framework achieves significant improve- ments in various cross-domain settings. In cross-dataset evaluations, it reduces average error by more than 27% compared to the baseline and improves by 6% over SOTA methods, with particularly outstanding performance in highly challenging scenarios.

---

2 Related Work
Gaze Estimation. Gaze estimation has shifted from using local eye features 26 20 to full-face inputs [38] which, despite including richer structural information, introduced domain-specific noise that impairs generalization. Existing solutions have largely focused either purifying 2D image features [3) or introducing prior knowledge. For instance, some methods incorporate geometric (32 [1]. However, these approaches often treat geometric priors as supplementary inputs rather than core representations. In contrast, our 3DGP module fundamentally reframes the problem by using parametric 3D model map faces from an unstructured pixel space to a physically meaningful and decoupled parameter space. This transforms the task from simple "pixel-to-gaze regression into more robust "structure-to-gazz prediction.
cues like head pose as auxiliary information [40] 38 22
Other works have leveraged large-scale pre-trained models like CLIP to enhance robustness [35] 36
These methods typically apply visual-linguistic knowledge general 2D image features. Our work differs significantly in its design. The SCAM module performs a targeted semantic purification on specific gaze code that has already been geometrically isolated by the 3DGP module. This novel "secondary purification" on a pre-decoupled feature is key contribution of our framework.
Face Parametric Model. 3D face models have evolved from the 3DMM first proposed by Blanz and Vetter, to BFM Face Warehouse (3] and then to FLAME[[)), gradually enhancing the modeling capabilities for facial shape, expression, and movement. Although recent models like [5T and FaceScape 34 offer higher geometric detail in appearance, they lack FLAME's capabilities in feature disentanglement and efficiency. FLAME's key advantage lies in its explicit parametric decoupling of shape, pose, and expression, a characteristic that highly aligns with the need in gaze estimation to separate gaze-related features from domain-specific interference. Additionally, achieves good balance between expressiveness and computational efficiency.
3 Method
3.1 3DPE-Gaze Framework Overview
To unlock the potential of 3D facial priors for generalized gaze estimation, we propose the 3DPE- Gaze framework. As shown in Figure 2 the 3DPE-Gaze framework consists of two core modules and final gaze predictor:
3D Geometric Prior Module (3DGP) The 3DGP module, based on the parametric face model FLAME, decomposes facial images into shape, expression, pose, lighting, and gaze parameters, achieving explicit decoupling of gaze features, facial structure features, and domain interference factors in physical space. This module also provides physically constrained anatomical foundations for gaze estimation through the reconstruction of 3D facial structures.
Semantic Concept Alignment Module (SCAM) The SCAM module utilizes CLIP pre-trained knowledge and contrastive learning strategies. This module explicitly distinguishes between "gaze- related concepts" (such as looking up-left) and "domain interference concepts" (such as lighting variations, facial expressions, etc.) in the feature space, providing cross-domain stable representations at the semantic level.
The Facial Prior-Gaze Predictor integrates facial structural features extracted by 3DGP with semantic concepts identified by SCAM. This decoder focuses on highly gaze-relevant geometric regions through attention mechanisms while suppressing domain-specifer interference such as background and lighting, generating the final accurate gaze prediction results.
Facial Prior-Gaze Predictor
This design successfully unlocks the potential of 3D facial priors in gaze estimation: the 3DGP module provides stable facial prior constraints, while the SCAM module compensates for FLAME's limitations in eyeball modeling, offering semantic-level supplements. The complementary fusion of both significantly enhances the model's cross-domain generalization capability. Technical details of each module will be elaborated in the following sections.
3.2 3D Geometric Prior Module (3DGP)
The 3DGP module decomposes input images into structured geometric parameter representations through parametric face model, thereby transferring gaze estimation from the pixel space, which
3

---

Shape code
Pose code
Expression code
Light code
code
Frozen
Trainable
① 3DGP- Geometric Prior Module
(Sec. 3.2)
Train
3D Facial Landmarks
FLAME
Multi-task Encoder
Sourse Domain
Full image containing
gazee noise
SCAM Semantic Concept Alignment Module
(Sec. 3.3)
3D Facial Priors
(Sec. 3.4)
lace with
lightngg."
Terelevant
Property
CLIP
Gaze-ärrelevan Features
Push
Cross Attention
Furgie
Encoder
lowards
Pull
code
Gaoe Predicted
Gaze Label
Ligae--align
Gase I Labell
"Loaking up and to the right?
FacePrior-Gaze Predictor
Figure 2: Overview of the proposed 3DPE-Gaze framework. The framework contains two core modules: (1) The 3D Geometric Prior Module (3DGP) utilizes the FLAME model decompose facial images into geometric parameters, achieving physical decoupling of gaze features and domain interference; (2) The Semantic Concept Aligmment Module (SCAM) separates gaze-related and unrelated concepts through CLIP-guided contrastive learning.
is susceptible to domain interference, to a more stable, decoupled parameter space. Through this decomposition, we can explicitly separate gaze code, facial structure parameters that affect gaze appearance, and domain-specific lighting parameters.
Parametric Representation and Encoding: Based the FLAME model we designed gaze- specific multi-task encoder that receives preprocessed facial images II, and outputs set of geometric parameters:
[8,0, tb 7.
where, 100 controls static facial shape, 0 R& represents head pose, e E 5g captures dynamic expressions, 2225 is gaze-specific encoding containing eyeball movement information, and 1 e 2² represents lighting parameters modeling environmental light conditions.
This parametric design decomposes facial images into comain-invvriian geometric factors and susceptible appearance factors. For example, head pose and expression parameters & remain relatively consistent under different lighting or skin color conditions, while lighting parameters / explicitly capture environmental variation information. This explicit decoupling significantly reduces the model's dependence on domain-specific appearance features.
To provide stable and structurally constrained facial parametric representations, reused the encoder architecture of the DECA[9] pre-trained model (based on ResNet backbone) and initialized all layers corresponding to the 3, 0, sb, parameters in the multi-task encoder with its pre-trained weights (including the shared ResNet backbone and corresponding output branches). During training, these DECA-initializer parts are completely frozen moon-trainable of facial structure parameters. In contrast, only the encoding branch responsible for outputting the gaze code 7 is trainable. Considering that the ? parameter specific mapping of generic features extracted by the ResNet backbone to gaze direction, we designed this branch as a multi-layer perceptron (MLP) connected after the shared ResNet backbone output features, able directly learn the mapping from robust features to gaze parameters, focusing gaze feature learning.
ensure the accuracy and stability
3D Reconstruction and Structural Constraints: After obtaining FLAME parameters, we recon- struct the 3D facial mesh through the FLAME decoder:
M pred FLAME 3,0, =T Bs(3) (p + (E
(2)

---

where T' is the average face template, (5) is the shape blend shape controlled by identity param- eters, (d) is the pose-related deformation, and (r f) is the expression blend shape. From the reconstructed facial mesh, we extract key 3D facial landmarks K E RNN33, which provide anatomy-based constraints for subsequent gaze decoding, ensuring that gaze predictions conform to physical laws.
Compared traditional methods, the 3DGP module transforms gaze estimation from black-box "pixel-to-gaze" mapping to a more structured "structure-to-gaze" prediction. By decoupling known geometric factors, enhances model interpretability and significantly improves cross-domain stability.
3.3 Semantic Concept Alignment Module (SCAM)
The SCAM module compensates for 3DGP's limitations semantic understanding by explicitly distinguishing between "gaze-related" and "gaze-imelevant" features the concept level through the powerful vision-language alignment ability of the CLIP
Systematic Generation of Semantic Concepts: To provide robust semantic supervision, employ a systematic methodology for generating language descriptions for both gaze-related (Sgare) and domain interference (Stomain) concepts.
For gazz-related concepts, template-bast based approach is utilized, conditioned on the ground-trut -trut gaze direction. We discretize gaze directions into canonical zones (e.g., "upper-right," "lower-left"), from which textual prompts such as, "A person looking towards the upper-right," are generated. This ensures the semantic representation is dynamically and accurately aligned with the precise directional gaze information for each sample.
For domain interference concepts, we leverage the physical parameters already extracted by our 3DGP module programmatically generate a diverse set of gaze-irrelevant descriptions. This strategy creates a strong, coherent link between our geometric decoupling and semantic purification stages. Specifically, these prompts describe: (1) Lighting conditions, derived from the light parameters / (e.g., "under strong light," "in dim light"); )) (2) Facial expressions, based on the expression parameters (c.g., "a neutral expression." "a person with a significant facial expression") "): (3) Facial shape, informed by the shape parameters f; and (4) Head pose, based on the pose parameters 0 (e.g., "head tilted to the left").
These systematically generated sets of descriptions are then passed through the CLIP text encoder to obtain their semantic representations:
Igaze VCLIP (&
Syaze Sgare
fdomaann CLIP VPCLI((Somman) (Adomain)
Sdomain E Stomain
where V-LI(()) represents the CLIP encoder.
Contrastive Learning and Concept Separation: To align gaze code 7 with gaze concepts in seman- tic space while keeping them away from domain interference concepts, we designed bidirectional contrastive loss:
Gaze Concept Alignment Loss:
agane-align log exp(sim(zy, (igaze) //gre)/T) Σ, exp(sim(22) ty)/T)
(5)
where z, is the projected gaze parameter, sim calculates cosine similarity, and is a temperature parameter. Domain Interference Repulsion Loss:
Ldomain
sim VCLI((s) m)
Sdomsin
*ESwmm
where m is a boundary parameter, ensuring that gaze features maintain sufficient distance from domain interference concepts.
Through this contrastive mechanism, the SCAM module establishes an explicit boundary between gaze features and domain interference features in semantic space, enabling the model to focus
5

---

high-level concepts related to gaze while filtering out domain-speciin interferences. Complementary to geometric priors, semantic priors demonstrate unique advantages in handling abstract visual concepts and complex visual interferences.
3.4 Facial Prior-Gaze Predictor
To fully utilize both geometric and semantic priors, we designed collaborative decoder that makes the two representations mutually enhancing through an attention mechanism, jointly guiding gaze prediction.
We adopt a cross-attention mechanism, using semantically guided gaze code y as the Query, and strce-dimensional geometrie landmarks K as the Key and Value:
Cross-modal Feature Fusion:
Fiused = CrossAttention (Q W : K = W W,K)
(7)
where Wa: Wk. W, learnable parameter matrices. This design enables the model selectively focus on key structural regions based on semantic understanding, increasing sensitivity to gaze-related features.
Gaze Vector Prediction: The fused features generate gaze predictions through the final MI.P decoder:
freg (Fusedd)
g |reg (P (Fiused
(8)
This collaborative mechanism achieves complementary enhancement of geometric and semantic priors: the structural features provided by 3DGP ensure that gaze predictions conform to physical constraints, while the semantic concepts contributed by SCAM guide the model to focus on the most relevant regions and suppress interference. Working together, they effectively alleviate the problem of traditional methods over-relying on domain-speciff appearance features while fully utilizing structured information within the full-face range.
3.5 Training Objectives and Implementation
We adopt a multi-objective joint optimization strategy to train the 3DPE-Gaze framework, with the overall loss function:
C = Å Langle + A Ggave--aign + Ag Aomomiinn-rppe
Gaze Angular Loss
Langle measures the angular difference between predicted gaze and true gaze:
g"g
Langle arccos
(10)
||
Gaze Concept Alignment Loss Cgaze-align encourages gaze parameters r to align with gaze-related concepts.
Leomain.repel ensures that gaze parameters y stay away from Domain Interference Repulsion Loss interference concepts.
Hyperparameters År A2, and Åg are used to balance the contributions of each loss term. Empirically, we set λ₁ = X2 = λa 1.0.
Training Implementation Details We conducted experiments on single NVIDIA A100 GPU, Specifically, we adopted a staged training strategy: first freezing the FLAME parameter branches and pre-training the gaze parameter y, then introducing contrastive learning of the SCAM module and jointly optimizing the entire model. We used the Adam optimizer with an initial learning rate of 10-1 and a batch size of 256.
4 Experiments
Datasets and Evaluation Protocol. We adopt experimental settings consistent with cutting- edge research in cross-domain gaze estimation 7 33 36. 35 [8] evaluating our method four

---

cross-domain tasks. Specifically, we use ETH-X Xaze [39 and Gaze360 as training datasets, and MPIIFaceGaze and EyeDiap 12 as testing datasets. For concise representation, we denote D (MPIIFaceGaze)) D - DD(EyeDiap), De((Gaze360)>> - DM. and Da - DD. This standardized cross-domain setup ensures that our experimental results can be directly compared with related research.
these four cross-domain tasks as D (ETTH-XXGazz) D
Data Preprocessing. For DE. Dw. and DD, we normalize facial images following the standard method in [37]; for Dg, we only select frontal face images to match the distribution characteristics of other datasets, which is consistent with former researches
18 All images are resized uniform resolution of 224x224 and normalized to the [0,1] range, thereby eliminating the influence of differences in acquisition devices and resolutions across different datasets.
4.1 Performance Comparison with State-of-the-Art Methods
Table 1: Performance comparison on cross-domain gaze estimation tasks (unit: degrees)
Method
DE - DM
D DD
De
DM
De Dr
Avg
CNN Baseline
8.56
8.90
9.51
8.48
8.86
PureGaze 7
7.08
7.44
9.28
9.32
8.28
CDG (30
6.73
7.95
7.03
7.27
7.25
Xu et al. 33
6.50
7.44
7.55
9.03
7.63
Liang et al. 18
5.79
6.96
7.06
7.99
6.95
CLIP-Gaze 36
6,41
7.51
6.89
7.06
6,96
35
6.45
7.22
6.83
6.86
6,84
Our 3DPF-Gaze
6.66
6.13
6.71
6.23
6.43
sholss the performance comparison between 3DPE-Gaze and existing state-of-the-ane methods on four cross-domain gaze estimation tasks. The results demonstrate that our method surpasses SOTA methods in 3 out of 4 cross-domain settings. From the overall performance, our approach exhibits stronger general generalization capability, achieving the lowest average error across all four cross cross-domain tasks, fully validating the excellent performance of our proposed framework in addressing cross-domain challenges. This result also confirms that effective utilization of facial priors can significantly enhance the cross-domain generalization capability of gaze estimation models.
Table 2: In-domain gaze estimation performance comparison (unit: degrees).
Method
within DM
within Dp
within Da
within Dg
Dilated-Net
6.19
13.73
N/A
Gaze360
4.06
5.36
11.04
4,46
RT-Gene
4.66
6.02
12.26
N/A
FullFace 38
4.93
6.53
14.99
7.38
RCNN 21
4.10
5.31
11.23
N/A
CA-Net 6
4.27
5.27
11.20
N/A
Gaze TirreTTRPPee TR-Puuee
5
4.74
5.72
13.58
N/A
GazeTR -R--Hidrrd
5
4.00
5.17
10.62
N/A
CNN Baseline
4.74
7.49
13.23
5,69
Our 3DPE-Gaze
4.03
5.06
11.83
4.39
While our 3DPE-Gaze framework primarily designed enhance cross- cross-domain generalization, it is also crucial to validate that this improvement does not compromise its performance within single domain. Therefore, we conducted in-domain experiments, with the results presented in Table The results show that our method achieves highly competitive, and in some cases state-of-the-arr performance. This demonstrates that our approach of leveraging 3D facial priors not only significantly boosts cross- dross-domain robustness but also maintains excellent accuracy for in-domain gaze estimation.

---

4.2 Diagnostic Analysis of Geometric Prior Quality
To understand the performance discrepancy across different tasks, particularly the weaker result on the DE + PM task, we conducted diagnostic analysis. We hypothesize that the model's final accuracy is strongly correlated with the quality of the geometric priors extracted by the 3DGP module.
To test this, prior quality. For any given subject, their physical IOD is fixed; therefore, a lower standard deviation of the IOD across multiple images indicates more stable and higher-quality 3D prior. As shown in Table 3 we grouped subjects from each target dataset into "High-Quality" and "Low-Quality" prior groups based this metric.
the stability of the reconstructed 3D Inter-ocular Distance (IOD)
proxy for
The results clearly show that for both target domains, the High-Quality Prior group achieves signifi- cantly lower gaze than the Low-Qualit, Prior group. This analysis confirms that model's s performance is indeed dependent on the quality of the extracted 3D geometry, and the instability of priors from the Das dataset is the primary reason for the higher error in that specific cross-domain task.
Table Diagnostic analysis of geometric prior quality and its impact on gaze error. Avg. IOD Std Dev refers to the average standard deviation of the reconstructed 3D Inter-ocular Distance.
Domain
Prior Quality Group
Avg. IOD Std Dev
Average Gaze Frror
Number of Subjects
DM
Prior
5.50
Low-Quality Prior
10.5
7.24
10
Overall
8.4
6,66
15
Dp
High-Quality Prior
3.8
5.38
9
Low-Quality Prior
9.2
7.10
7
Overall
6.2
6.13
16
4.3 Ablation Studies
Effectiveness of Core Modules
Our proposed framework relies on three complementary loss functions to achieve high-performance cross-domain gaze estimation: Gaze Angular Loss (Langle), Gaze Concept Alignment Loss (Learee align), and Domain Interference Repulsion Loss (Ldomain-ropet). To verify the contribution of each loss function and their synergistic effect, designed ablation experiments shown in Table
Table 4: Ablation experiments for different loss function combinations (unit: degrees)
Model Configuration
Dz
DM
DE
DD
De
Dx
De
Dp
CNN Baseline
8.56
8.90
9.51
8.48
+ Ldomain rupel
7.32
7.19
7.63
7.44
Caaze
8.03
8.35
8.40
7.87
+ SCAM (Cogue align + Lecemain repel)
7.18
6.86
7.32
7.17
3DGP Only (Lange)
7.45
7.19
7.60
6.80
+ Loveninneppel
6.90
6.50
7.05
6.35
Laaze alien
7.41
6.38
7.60
6.72
SCAM (Lgaze zliga Lecomii repel)
6.66
6.13
6.71
6.23
The results show that using the FLAME prior-based Lungle alone can significantly reduce errors, validating the fundamental value of geometric constraints. Further introducing Cyaze-align enhances model performance by leveraging semantic information. Finally, integrating Ldomain-repel effectively separates gaze-related and unrelated features, bringing comprehensive performance improvements, with the complete model achieving the best average error. This indicates that the synergistic effect of geometric constraints, semantic guidance, and feature decoupling crucial for improving cross- domain robustness.

---

Impact of Backbone Model Choices
To analyze the impact of our backbone model choices, we conducted ablation studies as suggested by reviewers, with results shown T For the semantic encoder, we found that while more powerful CLIP architectures can improve performance, our chosen ViT-B-16 provides strong balance between accuracy and efficiency. Similarly, for the FLAME parameter regressor, other high-quality models like SPECTRE also achieve competitive results, demonstrating our framework's s flexibility. We selected DECA as our primary regressor due to its wide availability and recognized strong performance.
Table 5: Ablation study the impact of different backbone models (CLIP text encoders and FLAME parameter regressors). Unit: degrees.
Backbone Component
DE + DM DE
DD
De - DM
De DD
Semantic Encoder (CLIP)
RN50
6.90
6.55
6.95
6.44
ViT-B-16 (Ours)
6.66
6.13
6.71
6,23
VIT-L-14
6,55
6.08
6.60
6.17
ViT-H-14
6.50
6.05
6.61
6.12
FLAME Parameter Regressor
EMOCA B
6,85
6.40
6.90
6.45
DECA (Ours)
6.66
6.13
6.71
6,23
SPECTRE 10
6.59
6.10
6.65
6.18
Optimization of Geometric Feature Transfer Paths
Table 6: Ablation experiments for model architecture configurations (unit: degrees).
Model Configuration
DE
DM
DE >DD
Dg >DM
DG-> Dp
Landmarks Only
6.66
6.13
6.71
6.23
Landmarks + Pose
7.20
6.70
6,85
6,47
Landmarks + Full Parameters
7.32
6.53
6,85
6,94
To identify the optimal geometric representation for generalization, compared three feature configurations from the FLAME model: using only 3D landmarks, landmarks with head pose parameters, and landmarks with the full parameter set. As shown Table 6 using only facial landmarks performed best across all tasks. This result suggests that landmarks provide a sufficiently structured abstraction of facial geometry, capturing key gaze-related relationships while filtering the domain-specific noise present in the more detailed parameters, Including the full parameter set introduced additional domain-spectie biases that hindered performance, confirming that focusing on abstract, invariant features is more effective for cross-domain learning.
4.4 Robustness Verification of Facial Priors in Extreme Scenarios
The core idea of our 3DPE-Gaze framework is to decouple gaze from other irrelevant features and incorporating 3D facial priors for generalized gaze estimation. Thus, in this section, we conduct experiments verify the robustness of ToPE-Gaze framework regarding varies factors, including head pose, lighting conditions, expression changes and glasses on the Dr - Das task.
Robustness under Extreme Lighting Conditions. As shown in Figure (left), method outper- forms the baseline under all lighting conditions, especially extremes. reduces errors by 8.3% in low-light and 10.6% in high-light areas, our 3D geometric representation effectively separates facial structure from environmental lighting effects.
Adaptability to Expression Changes. Figure (right) shows that our 3DPE-Gaze method consis- tently outperforms the baseline across the entire range of facial expressions. For extreme expressions (L.1 intensity > 17), our method maintains stable error level while the baseline's 's emror significantly increases. This due to our model's ability to decouple expression muscle activity from gaze direction.

---

Extination Error
Extimation
XV5 Card
04
10 Intensity Light
0.
14
Monee Pxpmosiion
Expression Magnitude (1.1)
Papweaee
Davin
Figure 3: Robustness analysis under extreme scenarios. Left: Analysis of gaze estimation accuracy under different lighting intensities; Right: Impact of facial expression variations on gaze estimation precision.
15A3
14
Estimation Error
Extimation Error
10
XPPE GanelOu Gane-nn
8sseline
0.0
0.2
0.4
0.6
0.8
Without Glasses
Witk Giavex
Head Pose Magnitude
Figure 4: Robustness analysis under extreme scenarios. Left: Gaze estimation accuracy under large head rotations; Right: Impact of glasses occlusion on gaze estimation precision.
Large Head Rotations. Head pose variation is major challenge in gaze estimation. As shown Figure 4 (left), our method outperforms the baseline across head poses, with the advantage growing larger angles. By effectively separating the compound effects of head pose and eyeball movement, our method maintains stable performance even in extreme poses where the baseline fails.
Generalization Capability in Glasses Occlusion Scenarios. As shown in Figure (right), our method significantly outperforms the baseline both without glasses (20.9% error reduction, -8.99% and with glasses (12.3% error reduction, - 16)). Most importantly, our model demonstrates excellent conditoonn stability, with an error difference of only 0.23 between the two conditions, compared to 1.30 for the baseline, ensuring more reliable user experience.
5 Conclusion
This paper introduces 3DPE-Gaze novel framework cross-domain gaze estimation that integrates 3D geometric and semantic priors. By leveraging 3DGP module for geometric decoupling and SCAM module for semantic purification, our method shifts the task from an unstable appearance- based space more robust geometric and semantic one. This design achieves state-of-the-art cross-domain performance without requiring any target domain data.
Limitations and Future Work. Limitations of our work include the FLAME model's difficulty in precisely modeling fine eye details and our reliance
predefined set of semantic concepts for contrastive learning. Future work will focus on three areas: developing specialized parametric eye models for finer detail; exploring adaptive semantic learning strategies, such as concept generation; extending the framework to dynamic, real-time applications on mobile and AR/VR devices.
Acknowledgement. This research has been supported by Beijing Natural Science Foundation (L244219).
10

---

References
Stefano Alletto, Andrea Palazzi, Francesco Solera, Simone Calderara, and Rita Cucchiara. Dr(eye)ve: dataset for attention-basee tasks with applications autonomous and assisted driving. In Proceedings of the IEEE Conference on Computer Vision and Puttern Recognition (CVPR) Workshops, June 2016.
[2] Volker Blanz and Thomas Vetter. Morphable Model For The Synthesis Of 3D Faces. Association for Computing Machinery, New York, NY, USA, 1 edition, 2023. ISBN 97984000788788. URL
//doi //doi.or//aii 100..114//359611.111 3596770
[3] Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. IEEF Transactions Visualization and Computer Graphics, 20(3):
415-425, 2014. doi: 10.1 10.1109/VVG.222132222.
[4] Zhaokang Chen and Bertram E Shi. Appearance based gaze estimation using dilated--onvollttion,, In Asian Conference on Computer Vision, pages 309-324. Springer, 2018.
[5] Yihua Cheng and Feng Lu, Gaze estimation using transformer. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 3341-3347. IEEE, 2022.
[6] Yihua Cheng, Shiyao Huang, Fei Wang. Chen Qian, and Feng Lu. A coarse-to-fie fine adaptive network for appearance-based gaze estimation. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 10623-10630, 2020.
Yihua Cheng, Yiwei Bao, and Feng Lu. Puregaze: Purifying gaze feature for generalizable gaze estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 436-443, 2022.
[8] Radek Dan&ček, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 20311-20322 2022.
[9] Yao Feng, Haiwen Feng, Michael Black, and Timo Bolkart. Learning an animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (ToG), 1-13,, 2021.
[10] Panagiotis P Filntisis, George Retsinas, Foivos Roussos, and Petros Maragos. Visual speech-aware perceptual 3d facial expression reconstruction from vidcos. arXiv preprint 2207 1094, 2022.
Athanasios Katsamanis, Anastasios
[11] Tobias Fischer, Hyung Jin Chang, and Yiannis Demiris. Rt-gene: Real-time eye gaze estimation in natural environments. In Proceedings of the European conference computer vision (ECCV), pages 334 452,, 2018.
[12] Kenneth Alberto Funes Mora, Florent Monay, and Jean-Mare Marc Odobez. Eyediap: a database for the development and evaluation of gaze estimation algorithms from rgh and rgb-d cameras. In Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA '14, page 255-258, New York, NY, USA, 2014. Association for Computing Machinery. ISBN 978145037750.. doi: 10.1 10.1145/258115825588199
URL httpssts::///oo http////iooor//////// 1ttp///0..11////11133333 2578190
[13] Christina Katsini, Yasmeen Abdrabou, George E. Raptis, Mohamed Khamis, and Florian Alt. The role of eye gaze in security and privacy applications: Survey and future hei research directions. In Proceedings of the 2020 CHI Conference Human Factors in Computing Systems, CHI '20, page 1-21, New York, NY, USA, 2020. Association Computing Machinery. ISBN 9781450367080. doi: 10.1145/3318831.3366884 URL BtRL:sttps://ool wrcelooo.orrggroooooo
org/10/ 10.
3376840
[14] Petr Kellnhofer. Adria Recasens. Simon Stent, Wojeiech Matusik, and Antonio Torralba. Gaze360: Physically unconstrained gaze estimation in the wild. In IEEE International Conference Computer Vision (ICCV), October 2019.
[15] Ruilong Li, Karl Bladin, Yajie Zhao Chinmay Chinara, Owen Ingraham. Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, et al. Learning formation of physically- face attributes. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 141003449, 2020.
[16] Tianye Li. Timo Bolkart, Michael J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph., 36(6), November 2017. ISSN 0730-0301, doi: 10.1 10800333800033000888 URL http:::tt///tt btops:://oo org/10. 0144/3130800 31308813
11

---

[17] Yingxi Li, Xiaowei Bai, Liang Xie, Xiaodong Wang, Feng Lu, Feitian Zhang, Ye Yan, and Erwei Yin. Real-time gaze tracking via head-eye cues head mounted devices. IEEE Transactions Mobile Computing, 139223313, 2024, doi: 10.1 N0.1100/TMC.222244255555
[18] Ziyang Liang. Yiwei Bao, and Feng Lu. De-conduddded gaze estimation. In Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, and Gui Varol. editors, Computer Vision - ECCV 2024, pages 219-235, Cham, 2025. Springer Nature Switzerland. ISBN 978 978-3-031-73337-4
[19] Congcong Liu, Yuying Chen, Lei Tai, Haoyang Ye, Ming Liu, and Bert Shi. A gaze model improves autonomous driving. 06 2019. doi: 10.1145/331411 11133111111.334466
[20] Feng Lu, Yusuke Sugano, Takahiro Okabe, and Yoichi Sato. Adaptive linear regression for appearance- based gaze estimation. IEEF transactions on pattern analysis and machine intelligence, 36( (6(10)2003320466 2014.
[21] C Palmero, J Sclva, MA Bagheri, and S Escalera. Recurrent enn for 3d gaze estimation using appearance and shape cues. arxiv 2018. arXiv preprint
[22] Sconwook Park, Shalini De Mello, Pavlo Molchanov, Umar Iqhal, Otmar Hilliges, and Jan Kautz. Few-shot adaptive gaze estimation. In Proceedings of the IEEF/CVF international conference on computer vision, pages 9368-9969 0368-9377, 2019.
[23] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal baxed surveillance, pages 296-301. Ieee, 2009.
[24] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763 PmLR. 2021.
[25] Vincent Sitzmann, Ana Serrano, Amy Pavel, Mancesh Agrawala, Diego Gutiérrez, and Gordon Wetzstein. Saliency in vr: How do people explore virtual environments? IEEE Transactions on Visualization and Computer Graphics, PP, 12 2016. doi: 10. 10.1109/TVCG.00
[26] Kar-Han Tan, David J Kriegman, and Narendra Ahuja. Appcarance-basel eye gaze estimation. In Sixth IEEE Workshop Applications of Computer Vision, 2002. 2002). Proceedings. pages 191-195. IEEE, 2002.
[27] Ashish Tawari, Kuo Chen, and Mohan Manubhai Trivedi. Where is the driver looking: Analysis of head, eye and iris robust gaze zone estimation. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 988-994, 2014. URL Utt.ttpp:://pp....amaaaaa.ooooo www..eamammccool......o org/ 19:002444
[28] Yunus Terzioglu, Bilge Mutlu, and Erol Sahin. Designing social cues for collaborative robols: The role of gaze and breathing in buman-robot collaboration. In Proceedingy of the 2020 ACM/IELE International Conference on Human-Robot Robot Interaction, HRI '20, page 343-357, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450367462.
doi: 10. 10.1145/3119502.374488
URL https
//doi. wtt.:o......../1111.113 1145/3319//33 3374829
[29] Haofei Wang, Xujiong Dong, Zhaokang Chen, and Bert Shi. Hybrid gaze/eeg brain computer interface for robot arm control on pick and place task. volume 2015, pages 1476-1479, 08 2015. doi: 10.1 109/EMBC. 2015.7318649.
[30] Yaoming Wang, Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong, and Tong Contrastive regression for domain adaptation on gaze estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 19376-19385, 2022.
[31] Zhimin Wang and Feng Lu.
Tasks reflected in the eyes: Egocentric gaze-aware visual task type IEEE Transactions Visualization and Computer Graphics, 30(11): ISSN 1077-2626. doi: 2.11109/TVCG.2224445661
recognition in virtual reality.
7277-7287, November 2024.
URL https
//doi 1f61i.org/10.110//TVV http://VCG22222..... 2024.345114
[32] Yunfeng Xiao, Xiaowei Bai, Baojun Chen, Hao Su. Hao He, Liang Xie, and Erwei Yin. De2gaze: Deformable and decoupled representation learning for 3d gaze estimation. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3091-31000 2025, doi: 10. 10.11094 9025.00294
12

---

[33] Mingjie Xu, Haofei Wang, and Feng Lu. Learning generalized gaze estimator from gaze gaze-consistent feature. In Proceedings of the Thirty-Seventh AAAI Conference Artificial Intelligence and Thirty-Fijh Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence, 23AAAA 880-0. doi: 10.1609/aaai v37.3 25446. URL htthhtts:://dd org/ 10gg/..../ 10. 1609/ aaai vv713...... 25406
AAAI Press, 2023, ISBN 178-1-57735-
[34] Haotian Yang, Hao Zhu, Yanru Wang. Mingkai Huang. Qiu Shen, Ruigang Yang. and Xun Cao. Facescape: large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the ieee/cvf conference computer vision and pattern recognition, pages 601-610, 2020.
[35] Pengwei Yin, Jingjing Wang, Guanzhong Zeng. Di Xie, and Jiang Zhu. Lg Lg-gaze: Learning geometry-awant continuous prompts for language-guide gaze estimation. In European Conference on Computer Vision, pages 1-17. Springer, 2024.
[36] Pengwei Yin, Guanzhong Zeng. Jingjing Wang, and Di Xie. Clip-gaze: towards general gaze estimation visual-linguisti model. In Proceedings of the AAAI Conference Artificial Intelligence, volume 38, pages 6729-6737, 2024.
[37] Xucong Zhang, Yusuke Sugano, Mario Fritz, and Andreas Bulling. It's written all over your face: Full-face appearance-basse bhssn gaze estimation. Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 2299-2308, 2017. doi: 10.1 109/CCVRR.2017...0
[38] Xucong Zhang, Yusuke Sugano, Mario Fritz, and Andreas Bulling. It's written all over your face: Full-face appearance-basee gaze estimation. In Proceedings of the IEFE conference on computer vision and pattern recognition workshops, pages 51-60, 2017.
[39] Xucong Zhang, Seonwook Park, Thabo Beeler, Derek Bradley, Siyu Tang, and Otmar Hilliges. Eth-xgaze: A large scale dataset for gaze estimation under extreme head pose and gaze variation. In Computer Vision ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part V. page 365-381, Berlin, Heidelherg. 2020. Springer-Verlag. ISBN 978-3-030-58557-0 1007/978- 3- 030 58558-7 7_22
doi:
10.10077978-3 3-030-585558-8 7 22.2. /tth:/ttt/://ooio
[40] Wangjiang Zhu and Haoping Deng. Monocular free-head gaze tracking with deep learning and geometry constraints. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: In the abstract and introduction, we present previous work on cross-domain gaze estimation and claim that our work improves gaze estimation accuracy by introducing facial priors. We provide evidence for our algorithm in the experimental chapter, which reflects the claims made in the abstract and introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: We have subsection in the conclusion to discuss the limitations of our method and future research directions.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged
the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
14

---

Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: This paper does not contain theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: This paper fully discloses all the information needed to reproduce the main experimental results.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to model. In general. releasing code and data often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
specific model and empirical evaluation, may
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (c.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing verifying the results.
15

---

5. Open access to data and code
Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [No]
Justification: We have provided sufficient information to reproduce the results, and we will release the data and code upon cleanup.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.cm cc/
public//giide//Cdelabbbisss oow.ssoboioiiooooooooooooo for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" an acceptable answer. Papers cannot be rejected simply for including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https for more details.
//nips /nipsscc/puut
guides PwoddSSumiissiooooolo...ooo
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification:
We have described all training details the paper.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: Due to limitations in computational resource access and training time, were unable to calculate error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
16

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: The computational resources required for the experiments are detailed in our paper.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (c.g., preliminary or failed experiments that didn't 't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification:
We fully comply with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (c.g.. if there special consid- eration due to laws or regulations in their jurisdiction..
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
There is socictal impact of the work performed.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
17

---

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
Our models are not high risk and do not require safeguards.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g.. code, data, models), used in the paper, properly credited and the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification:
We cited the creators and complied with the licenses and terms of use.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
18

---

If assets are released, the license, copyright information, and terms of use in the thcode has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
package should be provided. For popular datasets, paperswi
latasets.paperssit
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: Our paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moode as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsoureing or research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
19

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development in this research does involve LLMs as any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM for what should or should not be described.
20


Reviews:
[{"review_id": "ALc1HmPRTF", "reviewer": "Reviewer_KepF", "summary": "For gaze estimation task, existing methods usually introduce substantial irrelevant information, which can lead to overfitting and hinder the model’s generalization ability. To address this issue, this paper introduces 3DPE-Gaze, a novel framework that explicitly incorporates 3D facial priors to achieve feature decoupling and improve generalization in gaze estimation. Extensive experiments demonstrate that 3DPE-Gaze consistently outperforms existing state-of-the-art methods.", "questions": "1. First, please give more clearly statement for the contribution of the proposed model.\n2. To compare with more SOTA methods.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "Strengths:\nExisting full-face input approach presents a fundamental trade-off: although it preserves comprehensive gaze-related structural features, the critical eye region constitutes merely a minimal portion of the overall image.  To address this issue, first, this paper introduces the 3D Geometric Prior Module (3DGP), an innovative parametric encoder that fundamentally reformulates gaze estimation by mapping the problem from 2D pixel space to a 3D parametric domain encompassing head pose, facial expression, and shape parameters. Second, this paper presents the Semantic Concept Alignment Module (SCAM) to overcome the inherent constraints of geometric-only approaches in modeling intricate gaze semantics. The module strategically employs CLIP's pre-trained visual-linguistic knowledge combined with contrastive learning. Extensive experiments demonstrate the effectiveness of the proposed method.\nWeaknesses:\n1.\t1. The 3DGP module aims to decompose head pose, expression, shape, and gaze feature parameters. However, similar strategies have already been explored in previous studies, like [1]. In addition, this paper uses an existing method, FLAME, to decompose.\n2.\tIn the SCAM module, the key step is using CLIP to distinguish between gaze-relevant and irrelevant features through contrastive learning. However, after carefully reviewing Table 2, I found that most of the performance gains come from the Domain Interference Repulsion Loss, which is also driven by CLIP. Therefore, I have concerns about the novelty of this contribution.\n3.\tThis paper does not compare with several state-of-the-art methods, such as [2], and it does not achieve SOTA performance. Moreover, the aforementioned methods do not rely on CLIP, making the comparison presented in this paper less fair.\n[1] Cheng et al., “PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation”, 2022 AAAI.\n[2] Wang et al., “Contrastive Regression for Domain Adaptation on Gaze Estimation”, 2022 CVPR.", "was_revised": true, "final_justification": "I have thoroughly reviewed the authors' rebuttal and other reviewers' comments, I still concern novelty and the motivation. I therefore maintain my original rating.", "rebuttal": "**Dear Reviewer,**\n\nWe sincerely thank you for your detailed review of our paper, and especially for the clear and accurate summary of our work's core ideas and contributions you provided in the \"Strengths\" section.\n\nWe hope the following responses address your concerns and kindly ask you to consider the efforts we have made to improve the paper.\n\n---\n\n**Q1: Considering that previous studies (e.g., PureGaze [1]) have explored feature decomposition and this study uses the existing FLAME model, where does the innovation of the 3DGP module lie?**\n\n**A1:** The innovation of the 3DGP module lies in the introduction of **3D face priors**. This transforms the gaze estimation problem from the noise-susceptible 2D pixel space to a stable 3D parameter space, laying the foundation for extracting domain-invariant features. We do not simply apply the FLAME model to the task. Our contribution lies in how we have innovatively adapted and integrated it into an end-to-end gaze estimation framework.\n\nThis is fundamentally different from previous work:\n\n* Unlike Zhu & Deng [32], which relies on independent 2D image patches, our method is more integrated. Starting from a **single full-face image**, we directly decompose it into a unified **3D parameter space** via the FLAME model, achieving a more thorough modeling of physical priors.\n\n* Unlike Zhang et al. [30], which learns from the full face as 2D pixel features, we explicitly treat it as a **structured 3D object**. This fundamentally transforms the problem from \"pixel regression\" to a more robust and interpretable \"**parameterized regression**.\"\n\n* Unlike methods such as PureGaze [5] and FAZE [16] that **implicitly learn** decoupled features from 2D data, our 3DGP module adopts a \"**decoupling by design**\" philosophy. We leverage a powerful 3D face model as a prior to **explicitly** separate physically meaningful parameters, rather than relying on purely data-driven learning.\n\n----\n\n**Q2: Given that most of the performance gain comes from the CLIP-driven repulsion loss, what is the core novelty of the SCAM module?**\n\n**A2:** The core novelty of the SCAM module lies in its **bidirectional, complementary semantic decoupling mechanism**. The experimental data clearly show that while the **Domain Interference Repulsion Loss** ($\\mathcal{L}_ {domain-repel}$) is effective on its own, it must work in **synergy** with our other core component, the **Gaze Concept Alignment Loss** ($\\mathcal{L}_ {gaze-align}$), and the 3DGP module to achieve maximum efficacy.\n\n**Table 1: Ablation Study on the Internal Components of SCAM and Their Synergistic Effect with the 3DGP Module**\n| | Model Configuration | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | **Avg** |\n| :---: | :--- | :---: | :---: | :---: | :---: | :---: |\n| **Group One** | **On CNN Baseline** |\n| 1 | CNN Baseline | 8.56 | 8.90 | 9.51 | 8.48 | 8.86 |\n| 2 |  + $\\mathcal{L}_{domain-repel}$ | 7.32 | 7.19 | 7.63 | 7.44 | 7.40 |\n| 3 |  + $\\mathcal{L}_{gaze-align}$ | 8.03 | 8.35 | 8.40 | 7.87 | 8.16 |\n| 4 |  + SCAM ($\\mathcal{L}_ {gaze-align}$ + $\\mathcal{L}_ {domain-repel}$) | 7.18 | 6.86 | 7.32 | 7.17 | 7.13 |\n| **Group Two** | **On 3DGP** |\n| 5 | 3DGP Only ($\\mathcal{L}_{angle}$) | 7.45 | 7.19 | 7.60 | 6.80 | 7.26 |\n| 6 |+ $\\mathcal{L}_{domain-repel}$  | 6.90 | 6.50 | 7.05 | 6.35 | 6.70 |\n| 7 |  + $\\mathcal{L}_{gaze-align}$ | 7.41 | 6.38 | 7.60 | 6.72 | 7.02 |\n| 8 |  + SCAM ($\\mathcal{L}_ {gaze-align}$ + $\\mathcal{L}_ {domain-repel}$) | 6.66 | 6.13 | 6.71 | 6.23 | 6.43 |\n\n---\n\n**Q3: The paper fails to compare with some SOTA methods (e.g., Wang et al. [2]), does not achieve SOTA performance, and is it fair to compare CLIP-based methods with non-CLIP methods?**\n\n**A3:** After supplementing with relevant SOTA methods, we conducted a more comprehensive evaluation. The results clearly show that under a fair comparison benchmark, **our method achieves SOTA performance**, obtaining the lowest average error among all compared methods.\n\nOur original version did not include this method (Wang et al. [2]), mainly because it employed data augmentation strategies in its training that were not used by other baseline methods. As per the reviewer's suggestion, we have integrated this method (referred to as CDG in the table) and other relevant SOTA works into a new, more comprehensive performance comparison table. To ensure a consistent and fair comparison, our evaluation includes the latest SOTA methods that also utilize VLMs, namely CLIP-Gaze and LG-Gaze. To make this clearer, we have added a \"Uses VLM\" column in the table below to explicitly mark the methods that use vision-language models.\n\n**Table 2: SOTA Performance Comparison on Cross-Domain Gaze Estimation Tasks**\n| Task | Model | Uses VLM | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | Avg |\n| :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: |\n| DG | CNN Baseline | | 8.47 | 9.32 | 7.54 | 8.93 | 8.57 |\n| DG | PureGaze | | 7.08 | 7.48 | 9.28 | 9.32 | 8.29 |\n| DG | CDG | | 6.73 | 7.95 | 7.03 | 7.27 | 7.25 |\n| DG | Xu *et al.* | | 6.50 | 7.44 | 7.55 | 9.03 | 7.63 |\n| DG | Liang *et al.* | | **5.79** | 6.96 | 7.06 | 7.99 | 6.95 |\n| DG | CLIP-Gaze | ✓ | 6.41 | 7.51 | 6.89 | 7.06 | 6.97 |\n| DG | LG-Gaze | ✓ | 6.45 | 7.22 | 6.83 | 6.86 | 6.84 |\n| DG | **3DPE-Gaze (Ours)** | ✓ | 6.66 | **6.13** | **6.71** | **6.23** | **6.43** |"}, {"review_id": "QoNjyIrQDV", "reviewer": "Reviewer_uysX", "summary": "This paper proposes 3DPE-Gaze, a method for modeling 3D FLAME model parameters including the eyeball rotations. Along with using the FLAME model, the authors further suggest to remove gaze-irrelevant features from the FLAME gaze/eye parameters by using a contrastive loss in relation with CLIP text embeddings of a variety of descriptions. The authors use the DECA model for FLAME parameter regression, modifying it to also output the 256-dim FLAME eyeball parameters. These features are refined via cross-attention with predicted 3D facial landmarks. Typical cross-domain gaze estimation experiments are performed, where models are trained on either ETH-XGaze (D_E) or Gaze360 (D_G) and are evaluated on either MPIIFaceGaze (D_M) or EyeDiap (D_D). The results show improvements over state-of-the-art. A brief ablation study validates the losses used and the FLAME parameters used in refining the gaze feature. The authors provide multiple robustness analysis related graphs, showing that the proposed method improves in extreme cases in particular, where non-gaze related parameters such as lighting, expression, head pose, and glasses affect the baseline gaze model more extremely.", "questions": "1. The SCAM module could technically be used almost on its own, without needing the FLAME model. That is, most gaze estimation models produce gaze-relevant features only at their output, which the SCAM module could help improve. How would the performance of such a model compare with the proposed approach?\n2. Do you have any qualitative results to show how well DECA works on the evaluation datasets used in this paper?\n3. What was your reason for using cross-attention in your gaze predictor (Sec. 3.4)?\n4. Out of curiosity, does your 3DGP model allow for the driving/animation of rigged 3D avatars (incl. eye movements)? How well do the motions transfer?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The paper is written reasonably well with good quality figures. The overall concept and motivation is easy to understand, and the use of the FLAME face model is an interesting choice. The SCAM module's use of CLIP text embeddings as part of a contrastive loss is innovative.\n\nExperimentally speaking, the proposed method is only evaluated in cross-domain settings, where it does mostly well but poorly in the D_E to D_M evaluation (2nd column of Tab. 1). There are no within-dataset experiments shown, despite the possibility (very few parameters need to be trained as the approach is based off of DECA. Also, the ablation study section is quite minimal despite the fact that the proposed method is rather atypical. For instance, the choice of DECA and CLIP may be considered as factors worth varying (how does the model performance change when changing the FLAME regressor or semantic encoder model?).\n\nThe rather verbose and repetitive robustness section (Sec. 4.3) does show larger differences in errors at extremes (either end of the x-axis) and this is very cool as it shows that gaze-irrelevant features may have indeed been reduced thanks to the SCAM module. However, I wonder if the authors could have found other numerical ways of demonstrating this aspect as well, such as the variance of errors per subject (via per-subject violin plots).", "was_revised": true, "final_justification": "During the rebuttal process, the authors provided additional within-dataset experiment results which have made the proposed method very much competitive to the state-of-the-art. This reviewer also appreciates that further ablation studies were shared, to assess the cross-attention component of 3DGP, the choice of CLIP encoder and FLAME regressor models, and for providing an alternative \"robustness\" analysis. I have raised my final rating in accordance with the authors' responses and my understanding of the other reviewers' comments.", "rebuttal": "**Dear Reviewer,**\n\nWe sincerely thank you for the valuable time and effort you have dedicated to reviewing our manuscript. We greatly appreciate your recognition of our work, especially your comments that our **paper is well-written with good quality figures**, the **overall concept and motivation are easy to follow**, and your acknowledgment of the **innovative use of CLIP in the SCAM module**.\n\nWe hope the following responses address your concerns and kindly ask you to consider the efforts we have made to improve the paper.\n\n---\n**Q1: The model performs well in most cases, but why does it perform poorly on the  $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ task?**\n\n**A1:** Experimental analysis shows that the performance difference on this task **does not stem from a flaw in our method itself, but is directly correlated with the quality and stability of the 3D geometric priors extracted by our 3DGP module from different datasets**.\n\nWe designed an experiment to link the quality of the geometric priors to the final gaze estimation performance. We use the **stability of the reconstructed 3D Inter-ocular Distance (IOD) from keypoints** as a proxy metric for 3D reconstruction quality. For a given test subject, their physical IOD is fixed. When processing multiple images of the same subject, a large standard deviation in the calculated 3D IOD indicates that the extraction of 3D keypoints is unstable, which we define as a \"low-quality prior.\"\n\nFrom Table 1, we can conclude that: **our method is effective, but its performance depends on the quality of the 3D geometric priors. When the extracted 3D geometric priors are of poor quality, the limitations of the 3DGP module become apparent, thereby affecting the final performance.**\n\nWe will add these results and a detailed analysis to our revised manuscript to clarify the operational boundaries and preconditions of our model.\n\n**Table 1: Diagnostic Analysis of Geometric Prior Quality and Its Impact on Gaze Error**\n| Target Domain | Prior Quality Group | Average IOD Standard Deviation | Average Gaze Error | Number of Subjects |\n| :--- | :--- | :--- | :--- | :--- |\n| $\\mathcal{D}_{M}$ (MPIIFaceGaze) | High-Quality Prior | 4.1 | 5.50° | 5 |\n| | Low-Quality Prior | 10.5 | 7.24° | 10 |\n| | Overall | 8.4 | 6.66° | 15 |\n| $\\mathcal{D}_{D}$ (EyeDiap) | High-Quality Prior | 3.8 | 5.38° | 9 |\n| | Low-Quality Prior | 9.2 | 7.10° | 7 |\n| | Overall | 6.2 | 6.13° | 16 |\n\n---\n**Q2: The paper only shows cross-domain experimental results and lacks in-domain experiments. Does this mean the evaluation is incomplete?**\n\n**A2:** As suggested by the reviewer, we have added in-domain experiments. The results show that our 3DPE-Gaze framework not only excels in cross-domain tasks but also demonstrates excellent performance in in-domain tasks.\n\n**Table 2: In-domain Gaze Estimation Performance Comparison**\n| Methods | MPIIFaceGaze | EyeDiap | Gaze360 | ETH-XGaze |\n| :--- | :---: | :---: | :---: | :---: |\n| Dilated-Net | 4.42 | 6.19 | 13.73 | N/A |\n| Gaze360 | 4.06 | 5.36 | 11.04 | 4.46 |\n| RT-Gene | 4.66 | 6.02 | 12.26 | N/A |\n| FullFace | 4.93 | 6.53 | 14.99 | 7.38 |\n| RCNN | 4.1 | 5.31 | 11.23 | N/A |\n| CA-Net | 4.27 | 5.27 | 11.2 | N/A |\n| GazeTR-Pure | 4.74 | 5.72 | 13.58 | N/A |\n| GazeTR-Hybird | 4 | 5.17 | 10.62 | N/A |\n| --- | --- | --- | --- | --- |\n| CNN Baseline | 4.74 | 7.49 | 13.23 | 5.69 |\n| Our 3DPE-Gaze | 4.03 | 5.06 | 11.83 | 4.39 |\n\n\n---\n\n**Q3: The SCAM module is technically almost standalone. If the SCAM module were applied to a traditional gaze estimation method that does not rely on the FLAME model, how would its performance compare to your proposed full method?**\n\n**A3:** Yes, the SCAM module is effective on its own, and its combination with 3DGP is necessary and synergistic. We demonstrate this with the experiment in Table 3.\n\n**Table 3: Ablation Study of Core Modules**\n| Model Configuration | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | **Avg** |\n| :--- | :---: | :---: | :---: | :---: | :---: |\n| CNN Baseline | 8.56 | 8.90 | 9.51 | 8.48 | 8.86 |\n| Baseline+SCAM | 7.18 | 6.86 | 7.32 | 7.17 | 7.13 |\n| 3DGP only | 7.45 | 7.19 | 7.60 | 6.80 | 7.26 |\n| 3DPE-Gaze (3DGP + SCAM) | 6.66 | 6.13 | 6.71 | 6.23 | 6.43 |\n\n----\n**Q4: Are there any qualitative results that show how well the DECA model works on the evaluation datasets used in this paper?**\n\n**A4:** Yes, we use the standard deviation of the reconstructed 3D Inter-ocular Distance (IOD) as a proxy metric to evaluate the effectiveness of the DECA model. Since a person's physical IOD is fixed, a lower standard deviation of this metric indicates that the 3D geometric priors extracted by our model are more stable and of higher quality.\n\nAs shown in Table 4, the geometric priors extracted by our model on the EyeDiap dataset are more stable than those on MPIIFaceGaze.\n\n**Table 4: Quantitative Comparison of 3DGP Module Performance on Different Evaluation Datasets**\n| Target Domain | Average IOD Standard Deviation |\n| :--- | :---: |\n| $\\mathcal{D}_{M}$ (MPIIFaceGaze) | 8.4 |\n| $\\mathcal{D}_{D}$ (EyeDiap) | 6.2 |\n\n----\n**Q5: What is the reason for using cross-attention in your Facial Prior-Gaze Predictor (Section 3.4)?**\n\n**A5:** The core reason for using cross-attention is to allow the model to selectively focus on the most relevant regions of the facial geometry, which is superior to simple feature concatenation. As shown in the experiment in Table 5, the performance using cross-attention is significantly better than that of simple concatenation.\n\n**Table 5: Performance Comparison of Different Fusion Methods**\n| Model Configuration | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ |\n| :--- | :---: | :---: | :---: | :---: |\n| 3DGP ($L_{angle}$, concatenation) | 8.43 | 7.58 | 8.06 | 8.48 |\n| 3DGP ($L_{angle}$, cross-attention) | 7.45 | 7.19 | 7.6 | 6.8 |\n\n\n---\n\n**Q6: Does your 3DGP model support driving/animating a rigged 3D avatar (including eye movement)? How is the motion transfer effect?**\n\n**A6:** **Theoretically, our model fully supports driving a 3D avatar.** Building on our work, it is already possible to parse all the key parameters required to drive a FLAME model from a single image at once: head pose, expression, shape identity, and the eye rotation parameters that we have specifically decoupled.\n\nThis means we can control not only  the avatar's head movement and expressions but also achieve very **fine-grained eye movement control**. Therefore, we believe that the motion transfer effect should be quite good.\n\n---\n\n**Q7: The reviewer suggested a more in-depth ablation study, for example, exploring the impact of replacing the DECA or CLIP models on performance. What is your response?**\n\n**A7:** Following the reviewer's suggestion, we have explored the impact of replacing the **semantic encoder (CLIP) and the FLAME parameter regressor (DECA)** on the final model performance.\n\n**Table 6: Performance Comparison of Different CLIP Text Encoders**\n| CLIP Text Encoder | $\\mathcal{D}_  {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | **Avg** |\n| :--- | :---: | :---: | :---: | :---: | :---: |\n| RN50 | 6.90 | 6.55 | 6.95 | 6.44 | 6.71 |\n| **ViT-B-16 (Ours)** | **6.66** | **6.13** | **6.71** | **6.23** | **6.43** |\n| ViT-L-14 | 6.55 | 6.08 | 6.60 | 6.17 | 6.35 |\n| ViT-H-14 | 6.50 | 6.05 | 6.61 | 6.12 | 6.32 |\n\n\n**Table 7: Performance Comparison of Different FLAME Parameter Regressors**\n| FLAME Parameter Regressor | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | **Avg** |\n| :--- | :---: | :---: | :---: | :---: | :---: |\n| EMOCA | 6.85 | 6.40 | 6.90 | 6.45 | 6.65 |\n| **DECA (Ours)** | **6.66** | **6.13** | **6.71** | **6.23** | **6.43** |\n| SPECTRE | 6.59 | 6.10 | 6.65 | 6.18 | 6.38 |\n\nThe experimental results show that using a more powerful text encoder can indeed improve performance, and replacing the FLAME parameter regressor with other high-quality 3D face regressors can also achieve SOTA performance.\n\n---\n\n**Q8: The description in the robustness analysis section (4.3) is a bit verbose, and a better quantitative presentation (such as violin plots) was suggested.**\n\n**A8:** Due to the format and length constraints of the rebuttal, we are unable to attach violin plots directly here. We will add these more intuitive visualizations to the revised version of the paper to clearly show the error distributions.\n\n**To quantitatively respond to your suggestion at this moment, we have compiled detailed statistics on the error variance for each subject in the DE→DM task, summarized in the table below.** A lower error variance indicates that the model's prediction performance for that subject is more stable.\n\n\n**Table 8: Error Variance Comparison for Each Subject in the DE→DM Task**\n\n| Subject ID | CNN Baseline | Our 3DPE-Gaze | Subject ID | CNN Baseline | Our 3DPE-Gaze |\n| :--- | :---: | :---: | :--- | :---: | :---: |\n| p00 | 9.81 | **9.09** | p08 | 30.37 | **27.49** |\n| p01 | 4.93 | **4.84** | p09 | 12.85 | **9.35** |\n| p02 | 15.43 | **11.25** | p10 | 12.64 | **14.73** |\n| p03 | 10.76 | **10.70** | p11 | 10.03 | **9.92** |\n| p04 | 6.19 | **5.30** | p12 | 15.90 | **14.79** |\n| p05 | 11.73 | **7.82** | p13 | 12.24 | **11.95** |\n| p06 | 12.41 | **9.00** | p14 | 9.47 | **8.06** |\n| p07 | 26.52 | **23.84** | | | |"}, {"review_id": "xeNqUB19tJ", "reviewer": "Reviewer_ojVe", "summary": "This paper proposes a framework called 3DPE-Gaze, which combines the FLAME parametric model with CLIP-based concept disentanglement to map gaze representation into a physically interpretable 3D parameter space and a semantic concept space, thereby enhancing generalization performance. The proposed method consists of two modules: the 3D Geometric Prior (3DGP) encodes input images to obtain FLAME parameters and a gaze-specific code ($\\gamma$). Meanwhile, the Semantic Concept Alignment Module (SCAM) utilizes CLIP’s text embeddings to disentangle gaze-related concepts from domain interference concepts through contrastive learning by aligning the gaze-specific code $\\gamma$. The final gaze estimation is performed by the Facial Prior-Gaze Predictor, which takes $\\gamma$ and FLAME’s 3D facial landmarks as input. Cross-dataset experiments demonstrate performance surpassing the state-of-the-art in several settings and qualitatively and quantitatively show that the proposed method is advantageous for facial images under more extreme conditions.", "questions": "- Please clearly explain how the descriptions used by the SCAM module are prepared.\n- Please present, if any, discussions or evidence demonstrating the novelty or effectiveness of the SCAM module alone.", "limitations": "There is mention of technical limitations, but no specific reference is made to the potential negative societal impact of the gaze estimation task itself, such as privacy concerns.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 1, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths\n\n- The idea of separating input images into FLAME parameters and a gaze-specific code, while aligning the latter to gaze concepts via CLIP, is somewhat novel and technically interesting.\n- The method’s performance improvement across domains and under challenging conditions is demonstrated through experiments, making its impact significant.\n\n### Weaknesses\n\n- The approach of using facial landmarks as an auxiliary method for gaze estimation has been considered for a long time, and the basic idea of acquiring gaze-related concepts using CLIP’s knowledge is similar to CLIP-Gaze. When examining individual technical elements, significant technological novelty is not necessarily evident.\n- Section 3.3 does not clearly explain how the language descriptions used by the SCAM module are generated. In Figure 2, it appears that these are created by applying FLAME parameters to a prompt template, but this is uncertain without corresponding descriptions.\n- The use of CLIP in the SCAM module is technically more interesting than the idea of using FLAME in the 3DGP module, yet the effectiveness of this module on its own has not been demonstrated. In practice, the training approach using the SCAM module might also be applicable to conventional image encoders that perform simple feature disentanglement. While the complementary function of encoding by 3DGP and feature learning by SCAM aligns with intuition, a more detailed analysis is needed to determine whether this combination is truly essential. The current analysis does not provide a clear answer as to which idea contributes most to the improvement in accuracy.\n- There is a description that gaze estimation becomes \"interpretable structure-to-gaze prediction\" with 3DGP, but this might be a misleading claim. The $\\gamma$ obtained through the proposed method still appears to be a black-box feature, mostly independent of other geometric parameters.\n- The related work section is brief, but there should be various previous studies on feature disentanglement methods in gaze estimation and other individual ideas of this research. To accurately position the novelty of the proposed method within the history of research, broader citations and detailed comparative discussions are necessary.\n- There are overall English errors and inconsistencies in expressions like Semantic Concept Adversarial/Alignment Module and gaze feature/parameter/code, which hinder reader comprehension.", "was_revised": true, "final_justification": "The authors' rebuttal has helped my understanding of the novelty of the proposed method. Based on the discussion, I find no strong reason to oppose the paper's acceptance, so I raised the rating to Borderline Accept. However, I strongly urge the authors to revise the paper thoroughly, taking the review comments into account.", "rebuttal": "Dear Reviewer,\n\nWe sincerely thank you for the valuable time and effort you have dedicated to reviewing our manuscript. We greatly appreciate your recognition of our work, particularly your comments that our idea has a certain novelty and technical interest, and that the experiments demonstrate the method's performance improvement in cross-domain and challenging scenarios, making its impact significant.\n\nWe hope the following responses address your concerns and kindly ask you to consider the efforts we have made to improve the paper.\n\n---\n\n**Q1: How are the language descriptions used by the SCAM module prepared or generated?**\n\n**A1:** We designed a systematic set of text descriptions for the SCAM module, which is divided into two main categories: **gaze-related descriptions ($S_{gaze}$)** and **gaze-irrelevant descriptions ($S_{non-gaze}$)**.\n\n1.  **For gaze-related descriptions ($S_{gaze}$):**\n    Based on discretized gaze direction labels, we constructed five core templates corresponding to five typical gaze directions: \"top-left, top-right, center, bottom-left, and bottom-right.\" For example, when a sample's gaze direction label points to the upper-right, the system generates a corresponding text description, such as \"A person is looking to the upper right.\"\n\n2.  **For gaze-irrelevant descriptions ($S_{non-gaze}$):**\n    We constructed description categories covering various confounding factors based on the FLAME parameters and lighting conditions obtained from the 3DGP module:\n    * **(1) Lighting-related descriptions:** Based on the light code $l$, texts like \"strong lighting,\" \"weak lighting,\" or \"medium lighting\" are generated.\n    * **(2) Expression-related descriptions:** Based on the expression code $\\psi$, descriptions like \"a neutral expression\" or \"an obvious emotional expression\" are generated.\n    * **(3) Shape-related descriptions:** Based on the shape code $\\beta$, a simple text description of the person's facial features is generated.\n    * **(4) Pose-related descriptions:** Based on the pose code $\\theta$, the specific rotation of the head is described, for example, \"head tilted to the left\" or \"head rotated to the right.\"\n\nIn this way, we provide rich and structured positive and negative text samples for contrastive learning, enabling the model to distinguish gaze information from various domain-specific interferences. We will add a detailed description of the generation process above in Section 3.3 of the paper and update the caption of Figure 2 to ensure that readers can clearly and unambiguously understand the mechanism for generating this set of language descriptions.\n\n---\n\n**Q2: What is the independent effectiveness of the SCAM module? Is the combination of 3DGP and SCAM truly necessary?**\n\n**A2:** Yes, the SCAM module is effective on its own, and its combination with 3DGP is necessary and synergistic. We have demonstrated this through experiments, with the results shown in Table 1 below.\n\n**Table 1: Ablation Study of Core Modules**\n\n| Model Configuration | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | **Avg** |\n| :--- | :---: | :---: | :---: | :---: | :---: |\n| CNN Baseline | 8.56 | 8.90 | 9.51 | 8.48 | 8.86 |\n| Baseline+SCAM | 7.18 | 6.86 | 7.32 | 7.17 | 7.13 |\n| 3DGP only | 7.45 | 7.19 | 7.60 | 6.80 | 7.26 |\n| 3DPE-Gaze (3DGP + SCAM) | 6.66 | 6.13 | 6.71 | 6.23 | 6.43 |\n\nFrom this new experiment, we can draw two important conclusions:\n\n1.  **The SCAM module is effective on its own:** The performance of \"CNN Baseline + SCAM\" is significantly better than that of \"CNN Baseline.\" This proves that **even when combined with a traditional image encoder, our SCAM module can effectively enhance the model's generalization ability through semantic purification.**\n2.  **The combination of 3DGP and SCAM is necessary and synergistic:** The performance of our full model is far superior to any single-module-enhanced solution. This indicates that the stable geometric structure provided by 3DGP and the semantic purification provided by SCAM are **complementary**, and this combination is key to achieving optimal performance.\n\nWe will add this new ablation study to the experimental section of the paper to more clearly analyze the contribution of each component.\n\n---\n\n**Q3: Compared to previous work using facial landmarks or CLIP (e.g., CLIP-Gaze), where does the novelty of this paper lie?**\n\n**A3:** Our core contribution lies in the combination of **3D facial geometric priors** with a **semantic decoupling mechanism**. Our work is not a simple combination of existing tools but rather the construction of a novel framework that systematically addresses the cross-domain generalization problem.\n\n* **Difference from previous work using facial landmarks:** Traditional methods only use facial keypoints as an auxiliary coordinate input. In contrast, our 3DGP module uses FLAME to map the human face **from pixel space to a structured parameter space that is physically meaningful and decoupled**. This fundamentally changes the representation of the problem and is a deeper application of geometric priors.\n    1.  Unlike Zhu & Deng [32], which relies on independent 2D image patches, our method is more integrated. Starting from a **single full-face image**, we directly decompose it into a unified **3D parameter space** via the FLAME model, achieving a more thorough modeling of physical priors.\n\n    2.  Unlike Zhang et al. [30], which learns from the full face as 2D pixel features, we explicitly treat it as a **structured 3D object**. This fundamentally transforms the problem from \"pixel regression\" to a more robust and interpretable \"**parameterized regression**.\"\n\n    3.  Unlike methods such as PureGaze [5] and FAZE [16] that **implicitly learn** decoupled features from 2D data, our 3DGP module adopts a \"**decoupling by design**\" philosophy. We leverage a powerful 3D face model as a prior to **explicitly** separate physically meaningful parameters, rather than relying on purely data-driven learning.\n\n* **Difference from CLIP-Gaze:** CLIP-Gaze applies CLIP's knowledge to general 2D image features. In contrast, our SCAM module operates on a purer \"**gaze code ($\\gamma$)**\" that has already been **physically separated by 3DGP**. This \"secondary purification\" applied to a specifically separated feature is our unique design.\n\n---\n**Q4: Given that the gaze code ($\\gamma$) itself is still a black box, is it misleading to claim that 3DGP makes gaze estimation \"interpretable\"?**\n\n**A4:** The \"interpretability\" we refer to does not mean that every dimension of the $\\gamma$ vector has a clear physical meaning, but rather that the **3DGP module can explicitly decompose image information into multiple parameters that have clear physical meanings and can be independently analyzed**. We will use more rigorous phrasing in the revised manuscript.\n\n---\n\n**Q5: Language errors and terminological inconsistencies in the paper, and the brevity of the related work section.**\n\n**A5:** We have made the following revisions.\n\n* **Language and Writing:** We will conduct a thorough professional English proofreading of the entire manuscript to correct all grammatical errors and non-idiomatic expressions.\n* **Related Work:** We will expand the related work section to more comprehensively review feature decoupling methods in the field of gaze estimation [5, 13, 25], as well as other relevant techniques."}, {"review_id": "TpMR5kaFlS", "reviewer": "Reviewer_xsiZ", "summary": "Paper proposes a novel architecture for the cross-domain gaze estimation problem. The architecture consists 2 core modules: 3DGP and SCAM. Based on the FLAME model[13], the 3DGP extracts the explicit decoupling of gaze features, facial structure features, and domain interference factors in physical space. The SCAM module explicitly distinguishes between “gaze-related concepts” and “domain interference concepts” via foundation vision language model (CLIP). The FacePrior-Gaze Predictor module then integrates the features from the 3DGP and SCAM via cross-attention to predict the gaze.\n\nExperiments showed strong improvements over 3 out of 4 tests of the cross domain protocol, using ETH-XGaze[31 ]and Gaze360[10] as training datasets, and MPII FaceGaze[29] and EyeDiap[8] as testing datasets. Ablation studies were done to validate the core assumptions of the design (landmark features representation, robustness against various distractor features).", "questions": "See W2 and W3.", "limitations": "Yes. Also see W2 and W3.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths.\nS1. Paper is well written. The concepts are presented clearly with strong motivations, easy to understand technical details.\n\nS2. The hypothesis is well supported by the well-designed experiments and strong experimental results for the cross-domain protocol.\n\nS3. Good ablation studies were done to further strengthen the core hypothesis of the design.\n\nWeaknesses\nW1. The novelty and contribution is incremental as it builds upon existing works, like FLAME and CLIP with minor tweaks.\nW2. For completeness, it will be preferred to include the results for in-domain testing. Otherwise, it may raise the question of cherry-picking.\nW3. In-depth evaluation for the weaker results in the DE→DM setting will help reader to better understand the limitations of the proposed design. This is especially important, considering that the proposed method is worse than 4 SOTA methods. The gap between the best method (Liang et al) and the proposed method is quite big.", "was_revised": true, "final_justification": "Authors has sufficiently addressed my concerns and questions.\nNo change in my rating.", "rebuttal": "Dear Reviewers,\n\nWe sincerely thank you for your detailed review and insightful comments on our paper. We are honored that you found our paper to be well-written, with clear concepts and strong motivation (S1), and that our core hypotheses are supported by well-designed experiments and robust results (S2, S3). Your valuable feedback has shown us how to improve the manuscript, and for that, we are deeply grateful.\n\nNext, we will provide detailed answers to your questions, including results from additional experiments conducted based on your suggestions.\n\n---\n\n**Q1: The paper builds on existing work (e.g., FLAME and CLIP). Are the novelty and contributions incremental?**\n\n**A1:** Our core contribution lies in the combination of **3D facial geometric priors** with a **semantic decoupling mechanism**. Our work is not a simple combination of existing tools, but rather the construction of a novel framework that systematically addresses the cross-domain generalization problem. In A2 for Reviewer 2-ojVe, we demonstrated with experiments that **the SCAM and 3DGP modules are effective, and that their combination is necessary and has a synergistic effect.**\n\n**Table 1: Ablation Study of Core Modules**\n\n| Model Configuration | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {D}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {M}$ | $\\mathcal{D}_ {G}\\rightarrow\\mathcal{D}_ {D}$ | **Avg** |\n| :--- | :---: | :---: | :---: | :---: | :---: |\n| CNN Baseline | 8.56 | 8.90 | 9.51 | 8.48 | 8.86 |\n| Baseline+SCAM | 7.18 | 6.86 | 7.32 | 7.17 | 7.13 |\n| 3DGP only | 7.45 | 7.19 | 7.60 | 6.80 | 7.26 |\n| 3DPE-Gaze (3DGP + SCAM) | 6.66 | 6.13 | 6.71 | 6.23 | 6.43 |\n\n---\n\n**Q2: For completeness, it will be preferred to include the results for in-domain testing.**\n\n**A2:** As suggested by the reviewer, we have added in-domain experiments. The results show that our 3DPE-Gaze framework not only excels in cross-domain tasks but also demonstrates excellent performance in in-domain tasks.\n\n**Table 2: In-domain Gaze Estimation Performance Comparison**\n| Methods | MPIIFaceGaze | EyeDiap | Gaze360 | ETH-XGaze |\n| :--- | :---: | :---: | :---: | :---: |\n| Dilated-Net | 4.42 | 6.19 | 13.73 | N/A |\n| Gaze360 | 4.06 | 5.36 | 11.04 | 4.46 |\n| RT-Gene | 4.66 | 6.02 | 12.26 | N/A |\n| FullFace | 4.93 | 6.53 | 14.99 | 7.38 |\n| RCNN | 4.1 | 5.31 | 11.23 | N/A |\n| CA-Net | 4.27 | 5.27 | 11.2 | N/A |\n| GazeTR-Pure | 4.74 | 5.72 | 13.58 | N/A |\n| GazeTR-Hybird | 4 | 5.17 | 10.62 | N/A |\n| --- | --- | --- | --- | --- |\n| CNN Baseline | 4.74 | 7.49 | 13.23 | 5.69 |\n| Our 3DPE-Gaze | 4.03 | 5.06 | 11.83 | 4.39 |\n\n---\n\n**Q3: Why does the model perform poorly on the $\\mathcal{D}_ {E}\\rightarrow\\mathcal{D}_ {M}$ task, and what limitations of the model does this reveal?**\n\n**A3:** Our experimental analysis shows that the performance difference on this task **does not stem from a flaw in our method itself, but is directly correlated with the quality and stability of the 3D geometric priors extracted by our 3DGP module from different datasets**.\n\nTo quantify and validate this hypothesis, we designed an experiment to link the quality of the geometric priors to the final gaze estimation performance. We use the **stability of the reconstructed 3D Inter-ocular Distance (IOD) from keypoints** as a proxy metric for 3D reconstruction quality. For a given test subject, their physical IOD is fixed. If the model, when processing different images of the same subject, calculates a large standard deviation for the 3D IOD, it indicates that the extraction of 3D keypoints is unstable, which we define as a \"low-quality prior.\"\n\nThe experimental results strongly support our hypothesis and clearly demonstrate the conclusion: **Our method is effective, but its final performance depends on the quality of the 3D geometric priors. When the extracted 3D geometric priors are of poor quality, the limitations of the 3DGP module become apparent, thereby affecting the final performance.**\n\nWe will add these results and a detailed analysis to our revised manuscript to clarify the operational boundaries and preconditions of our model.\n\n**Table 3: Diagnostic Analysis of Geometric Prior Quality and Its Impact on Gaze Error**\n| Target Domain | Prior Quality Group | Average IOD Standard Deviation | Average Gaze Error | Number of Subjects |\n| :--- | :--- | :--- | :--- | :--- |\n| $\\mathcal{D}_{M}$ (MPIIFaceGaze) | High-Quality Prior | 4.1 | 5.50° | 5 |\n| | Low-Quality Prior | 10.5 | 7.24° | 10 |\n| | Overall | 8.4 | 6.66° | 15 |\n| $\\mathcal{D}_{D}$ (EyeDiap) | High-Quality Prior | 3.8 | 5.38° | 9 |\n| | Low-Quality Prior | 9.2 | 7.10° | 7 |\n| | Overall | 6.2 | 6.13° | 16 |"}]

Author rebuttal:

Meta-review: This paper initially received mixed reviews, but through the rebuttal and discussion phase, the reviewers converged toward an overall positive opinion. The level of technical novelty was a point of disucssion, and I am convinced that the paper provides meaningful and novel contributions to the community. The authors's rebuttal and discussion addressed some reviewers’ concerns to a satisfactory degree.

I recommend acceptance of this paper.
Decision: Accept (poster)