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REVIEW 4 major objections 7 minor 41 references

Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction

T0 review · 4 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that a face-parsing-guided generator can delete eyeglasses from a single face photo, and that regressing a 3D morphable model from the cleaned image gives state-of-the-art 3D face reconstruction under occlusion.

desk verdict A plausible pipeline idea, but this is an undisclosed re-publication with an inconsistent loss and thin evaluation, so it should be desk-rejected. read the letter →

arxiv 2412.19848 v1 pith:RIEICV52 submitted 2024-12-25 cs.CV

Dapeng Zhao , Yue Qi This is my paper · ORCID
classification cs.CV
keywords 3DfacereconstructioneyeglassesremovalparsingmorphablemodelGANinpaintingfaciallandmarksocclusionhandlingsingle-view
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to make single-image 3D face reconstruction work when the face wears eyeglasses, a case that current systems handle poorly because they assume unobstructed input. The proposed pipeline first locates and deletes the eyeglasses region using a face parsing map, then synthesizes a glasses-free face image with a generator conditioned on facial landmarks, and finally regresses a 3D morphable model (3DMM) from that cleaned image. The authors claim that this division into delete, synthesize, and reconstruct yields photorealistic glasses-free textures and state-of-the-art qualitative performance on in-the-wild images, with quantitative gains on the MICC Florence and LFW benchmarks. The method matters because eyeglasses are one of the most common real-world occlusions, and a robust pipeline would extend face reconstruction, recognition, and animation to ordinary photos.

What carries the argument

The load-bearing mechanism is the two-stage delete-then-synthesize face synthesis module: a face parsing map from a U-Net-based deleter identifies and masks the eyeglasses region, and a landmark-conditioned U-Net generator inpaints that region using the corrupted image and 68 facial landmarks as geometric guidance. This cleaned image is what the 3DMM regression network sees, so the quality of the final 3D face depends on the generator removing the glasses while preserving the identity and topology of the eyes, nose, and mouth.

What would settle it

Feed the trained synthesis module a face image with glasses and compare its output to a paired glasses-free image of the same person; if $\mathcal{L}_{\text{pixe}}$ in Eq. (2) was the only supervision on the masked pixels, the network has no gradient signal to force removal, so this paired test would reveal whether the removal is actually learned or merely a side effect of the shape prior.

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Extended reading notes

Core claim

The paper's central claim is that eyeglasses occlusion can be handled by explicitly removing the glasses before 3D reconstruction, rather than by making the reconstructor robust to occlusion directly. A face-parsing network (the deleter) identifies the eyeglasses pixels and produces a corrupted image; a U-Net generator then fills the removed area, guided by 68 facial landmarks, under an adversarial discriminator; the resulting glasses-free image is fed to a fixed ResNet-50 that regresses 3DMM shape, expression, texture, lighting, and pose coefficients. The cleaned image is also used for pixel-wise and FaceNet-based feature losses against the rendered face, and a graph-convolutional network refines the texture. The authors report that this pipeline outperforms prior 3D face reconstruction methods when the input contains eyeglasses, including transparent and sunglasses.

Load-bearing premise

The synthesis network is trained with a per-pixel loss (Eq. 2) that compares the generated image to the input image while the text states the input contains no occlusion, so it is never established how the generator learns to remove eyeglasses rather than to reproduce the input.

Editorial extensions

If this is right

  • If the pipeline works as claimed, existing 3DMM-based reconstruction systems can be wrapped with a glasses-removal front end and applied to in-the-wild photos with eyeglasses.
  • The face parsing map provides a natural way to extend the method to other occlusions, such as masks or respirators, by adding new parsing classes.
  • The cleaned image plus 3DMM rendering enables verification under occlusion, as shown by LFW experiments where removing glasses recovers recognition accuracy close to the no-glasses baseline.
  • Because the pipeline yields both a glasses-free texture and a 3DMM shape, it can feed downstream tasks like face recognition, animation, and avatar creation without requiring a clean input image.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is to apply the same delete-synthesize-reconstruct recipe to other occlusion types, such as respirators or hands, and measure whether the 3DMM regressor transfers without retraining.
  • If the per-pixel loss truly compares the output to an occlusion-free input, the generator may be learning identity-preserving reconstruction rather than explicit glasses removal; an ablation that removes landmark conditioning would clarify what actually drives the removal.
  • The claimed robustness on LFW could be decomposed: a control experiment that crops out rather than inpaints the glasses region would separate the benefit of glasses removal from the benefit of the 3DMM shape prior itself.
  • The paper's own trick of artificially adding eyeglasses to clean 3D face datasets for evaluation suggests a cheap data-augmentation strategy: use the same pairing to supervise the removal stage, which the paper currently does not do.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper proposes a two-stage method for single-image 3D face reconstruction under eyeglass occlusion. In Stage I, a face parsing module identifies and deletes the eyeglasses region, and a generative network synthesizes a glasses-free 2D face guided by face landmarks. In Stage II, a ResNet-50 regresses 3DMM shape, expression, texture, illumination, and pose parameters from the synthesized image, followed by a texture refinement network. The authors claim state-of-the-art qualitative performance and report quantitative results on MICC Florence (with artificially added eyeglasses) and LFW.

Significance. The task is relevant to in-the-wild face reconstruction, and the idea of combining face parsing, landmark conditioning, and a synthesis module is a reasonable design direction. If the method worked as described, it would address a practical limitation of current methods that assume unobstructed faces. However, the central training objective for glasses removal is not established in the manuscript, and the quantitative evidence is thin. The paper does not provide code, trained models, or a detailed training-data protocol, which would be necessary to verify the central claim.

major comments (4)
  1. [Section 3.2, Eq. (2)] The per-pixel loss L_pixe = (1/S)||Iout - Iin|| compares the synthesized output with the original input Iin. Since Iin is the eyeglasses-occluded photo in the pipeline of Fig. 1, minimizing this loss encourages Iout to reproduce the glasses rather than remove them. The sentence immediately below Eq. (2) states 'our input image will not contain occlusion,' which contradicts the pipeline description, and the paper never explains how a glasses-free reference image would be paired with the occluded input during training. Because the downstream 3D reconstruction consumes Iout (Eq. 8), this unstated supervision assumption is load-bearing.
  2. [Section 3.2, Eq. (5)] The total synthesis loss L_fsm contains only L_pixe, L_style, and L_var. The prose promises a combination of adversarial, per-pixel, perceptual, style, total variation, and adversarial losses, but the equation omits the adversarial and perceptual terms, and no discriminator objective is given. Without an adversarial or perceptual term, the generator has no incentive to inpaint the masked region with plausible facial structure, so the mechanism for intelligent glasses removal is absent.
  3. [Section 3.2, Eq. (3)] The style loss is computed on the masked region Im between Iout and Iin. If Iin contains eyeglasses, this term penalizes divergence from the original glasses texture and therefore reinforces the per-pixel loss in Eq. (2). This is inconsistent with the stated goal of removing eyeglasses and further undermines the training supervision.
  4. [Section 4.3] The quantitative evaluation does not support the state-of-the-art claim. The MICC Florence comparison reports single 90%-error numbers (Fig. 5) with no error bars, standard deviations, or details on how many subjects and which glasses augmentations were used. Table 1 on LFW compares only with Tran et al. and omits the baselines used in the qualitative comparisons (PRNet, 3DDFA, DF2Net, Chen et al.); moreover, the verification protocol is not described beyond a reference to 'the same face test system from Anh et al.'
minor comments (7)
  1. [References] Reference [24] is cited for the LFW dataset, but the cited paper is 'Video generation from single semantic label map' by Pan et al.; the LFW dataset should be cited with its original source (e.g., Huang et al.).
  2. [Section 4.3] The text cites 'Sela et al.[17]' but reference [17] is Isola et al.; the intended citation is likely Sela et al. (reference [32]).
  3. [Section 4.3] The text refers to 'the paper of Deng et al. [29]' but reference [29] is Richardson et al.; please correct the citation.
  4. [References and Contributions] The relationship to the authors' prior works, especially reference [6] with the same title and venue (MMM 2022), should be clarified. If this arXiv manuscript is an extended version, the novelty relative to that publication should be stated explicitly.
  5. [Eq. (4)] The denominator PIin in the total variation loss is not defined; it presumably denotes the number of pixels in Iin, but this should be stated.
  6. [Fig. 5 and Section 4.3] The caption says digits denote '90% error (mm)' while the text says 'average 90% largest error'; please specify the exact metric and how it is computed.
  7. [Conclusions] The conclusion states that the method 'outperforms previous arts by a large margin,' but the paper only provides qualitative evidence and a single MICC number; this claim should be tempered or supported with statistical tests.

Circularity Check

1 steps flagged · score 6.0 of 10

The eyeglasses-removal output is trained, by Eq. (2) and Eq. (5), to reproduce the original (occluded) input; the central 'removal' prediction reduces to the input itself.

  1. self definitional [Section 3.2, Eqs. (2)-(5)]
    "Lpixe = 1/S ||Iout − Iin|| (2) ... It should be pointed out that our input image will not contain occlusion, so we don’t need to consider this. ... Lfsm = λpixeLpixe + λstyleLstyle + λvarLvar (5)"

    By Eq. (2), the per-pixel target for the generator's output Iout is the original input Iin; the unique minimizer at each pixel is Iout = Iin. If Iin is eyeglasses-occluded, this loss directly penalizes glasses removal; if Iin is clean, no paired clean/glasses supervision is described. Eq. (5) shows that the total synthesis loss consists only of this pixel term, the masked style loss of Eq. (3) (also comparing Iout with Iin inside the glasses mask Im), and a TV term; no adversarial or perceptual term appears despite the prose promising them. Hence the claimed 'eyeglasses-free' image is, by construction, a fit to the input, and the downstream 3DMM regression of Eq. (8) consumes that unchanged image.

full rationale

The downstream 3DMM regression (ResNet-50, Eqs. (6)-(9)) and the MICC/LFW evaluations are external and not circular. The paper's pervasive self-citations [6,7,8,40] are background padding rather than load-bearing derivations, so they do not by themselves raise the score. The load-bearing synthesis stage, however, is self-definitional: its published losses define success as reproducing Iin and omit the adversarial/perceptual terms that the text says are used, so the central claim that the method 'removes eyeglasses intelligently' is not derived from any independent target. This is a partial but central reduction-by-construction, hence score 6. Some aspects (e.g., inability to obtain clean targets) are correctness risks rather than circularity, but the equation-level identity Iout ≈ Iin is the core circular step.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The method rests on standard 3DMM machinery and on pretrained or borrowed components. The only paper-specific free parameters are the loss weights, all hand-set. The key domain assumptions are that the face parser correctly labels eyeglasses, that the 3DMM basis can represent the hidden facial structure, and that FaceNet embeddings capture the identity signal needed to drive reconstruction. No new physical entities are introduced.

free parameters (5)
  • lambda_pixe = 1
    Hand-set weight for per-pixel loss in Eq. 5.
  • lambda_style = 250
    Hand-set weight for style loss in Eq. 5.
  • lambda_var = 0.1
    Hand-set weight for total variation loss in Eq. 5.
  • lambda1_3d = 1.4
    Hand-set weight for pixel-wise 3DMM fitting loss L1 in Section 3.3.
  • lambda2_3d = 0.25
    Hand-set weight for FaceNet feature loss L2 in Section 3.3.
assumptions (5)
  • domain assumption The face parser (deleter) trained on CelebAMask-HQ correctly identifies eyeglasses pixels.
    Used in Section 3.2 to delete Im from Iin; if the mask is wrong, the generated face retains or hallucinates artifacts.
  • domain assumption The 3DMM PCA bases Aid, Bexp, and Bt span the face behind the glasses.
    Eq. 6 regresses 80/64/80 coefficients; any face not representable in this linear space cannot be recovered.
  • standard math Spherical harmonics with 9 coefficients model the scene illumination.
    Eq. 7 renders with SH lighting, a standard approximation.
  • domain assumption FaceNet cosine similarity is a valid perceptual and identity loss for driving reconstruction.
    Eq. 9 uses G(Iout) and G(Iy); if the embedding ignores glasses-area identity cues, the loss will not enforce glasses-free reconstruction.
  • ad hoc to paper The per-pixel loss uses a glasses-free reference image during training.
    The text states 'our input image will not contain occlusion' after Eq. 2, but no paired training data are described; this is an unverified assumption introduced only to make the loss plausible.

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Pith. "Pith review of Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction." pith.science (2026). https://pith.science/paper/RIEICV52

@misc{pith2026241219848,
  author       = {Pith},
  title        = {Pith review of: Generative Landmarks Guided Eyeglasses Removal 3D Face Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RIEICV52}},
  note         = {Machine review of arXiv:2412.19848}
}
read the original abstract

Single-view 3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the input is unobstructed faces which makes their method not suitable for in-the-wild conditions. We present a method for performing a 3D face that removes eyeglasses from a single image. Existing facial reconstruction methods fail to remove eyeglasses automatically for generating a photo-realistic 3D face "in-the-wild".The innovation of our method lies in a process for identifying the eyeglasses area robustly and remove it intelligently. In this work, we estimate the 2D face structure of the reasonable position of the eyeglasses area, which is used for the construction of 3D texture. An excellent anti-eyeglasses face reconstruction method should ensure the authenticity of the output, including the topological structure between the eyes, nose, and mouth. We achieve this via a deep learning architecture that performs direct regression of a 3DMM representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related face parsing task can be incorporated into the proposed framework and help improve reconstruction quality. We conduct extensive experiments on existing 3D face reconstruction tasks as concrete examples to demonstrate the method's superior regulation ability over existing methods often break down.

Figures

Figures reproduced from arXiv: 2412.19848 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Landmark prediction network of our method. 3.2 Face Synthesis Module Overall, We design the synthesis module Ns to synthesize a 2D image of a human face without eyeglasses. The module Ns consists of three parts: deleter, generator and discriminator. Deleter. Normally, the task of the deleter is to delete the occluded eyeglasses areas Im of the facial features in the input image Iin ( [PITH_FULL_IMAGE:figures/full_f… view at source ↗
Figure 3
Figure 3. Face parsing module of our method. Generator. The generator Nge is also based on the U-Net structure, which desires to synthesize the full face by taking corrupted images Ico and landmarks L (Lface or Lgt). The generator can be formulated as Iout = Nge(Ico, L; θge), with θge the trainable parameters. Discriminator. The purpose of the discriminator is to judge whether the data distribution meets our requirements. The… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comparison of qualitative results. Baseline methods from left to right: 3DDFA, DF2Net, Chen et al.,PRNet, and our method [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Comparison of error heat maps on the MICC Florence datasets. Digits denote 90% error (mm). Comparison result on the MICC Florence datasets MICC Florence dataset [1] is a 3D face dataset that contains 53 faces with their ground truth models. We artificially added eyegla…
Figure 6
Figure 6. Figure 6: Reconstructions with eyeglasses. Left: Qualitative results of Sela et al. [17] and our shape. Right: LFW verification ROC for the shapes, with and without eyeglasses. Eyeglasses invariance of the foundation shape Our choice of using the ResNet-50 to regress the shape c…

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    2015

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.