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REVIEW 4 major objections 5 minor 42 references

3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A single color photo of an occluded face becomes a detailed 3D shape by removing the occluder in 2D, then adding a learned bump map to a coarse morphable model.

desk verdict Load-bearing gaps in the bump-map training signal and a contradiction about the synthesis network make the core claim unevaluable; the pipeline is coherent but not referee-ready. read the letter →

arxiv 2412.19849 v1 pith:B6NKITAZ submitted 2024-12-25 cs.CV

Dapeng Zhao , Yue Qi This is my paper · ORCID
classification cs.CV
keywords 3Dfacereconstructionbumpmappingparsingoccludedscenesimagesynthesisgeometrydetailssingleedgelines
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

Single-image 3D face reconstruction normally produces smooth shapes and strong textures, and it fails when glasses, hair, or hands cover part of the face. This paper proposes a two-step pipeline: first it removes the occluder in 2D by combining face parsing and edge-line maps, synthesizing an unobstructed frontal image; then it regresses a coarse 3D morphable shape and adds mid-level surface detail with a learned bump map. The central claim is that this unified design recovers plausible geometry details and stable global shapes from occluded real-world photos, where texture-focused baselines break down. If the claim holds, single-image reconstruction can preserve wrinkles and expression folds without multiview or depth input, even under occlusion.

What carries the argument

The load-bearing mechanism is the bump map $\Phi$ added to the base 3D morphable model: a grayscale depth-displacement field that lets the pipeline add mid-level wrinkles and folds without regressing high-resolution geometry directly. Around it sit two supporting modules: a face parsing map generator with edge-aware graph reasoning, and a face edge-line generator built from stacked encoder-decoder networks with message passing between visible and eyeglass-covered regions plus an adversarial effectiveness discriminator. These maps feed a face image synthesis step that regenerates the unobstructed frontal image, and the final parameter vector $y = (\alpha_{id}, \beta_{exp}, \beta_t, \gamma, p) \in \mathbb{R}^{239}$ covers identity, expression, texture, illumination, and pose while detail is delegated to the bump map.

What would settle it

Run the pipeline on a set of photos whose ground-truth high-resolution 3D face scans are known, and measure the distance between the reconstructed surface and the scan around the eyes; if the bump-mapped shape is no closer to the scan than the bare morphable-model shape, or if no ground-truth bump map can be obtained to train Equation (6), the central detail-recovery claim fails.

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Extended reading notes

Core claim

The paper claims that mid-level facial geometry details can be recovered from a single color image even when parts of the face are occluded, by separating a stable coarse shape estimate from a detail estimate. The coarse shape comes from a weakly supervised ResNet regressor applied to a synthesized glasses-free frontal image; the detail comes from a bump map $\Phi$ that encodes per-pixel depth displacement relative to the base shape, so the detailed depth is $d'(b) = d(b) + \phi^{-1}(\Phi(b))$. The training loss for this detail stage combines an L1 term on the bump map with L1 terms on its 2D gradients, which the authors report suppresses noise while preserving high-frequency detail. Their stated result is high-quality reconstruction with geometry details from captured facial images under occluded scenes, with the eyeglass-removal stage as the key to making the global shape regression stable.

Load-bearing premise

The geometry-detail loss in Equation (6) assumes a ground-truth bump map $\tilde{\Phi}$ is available for training, but the paper never says where that map comes from; without that supervision, the claim of adding geometry details cannot even be evaluated.

Editorial extensions

If this is right

  • Reconstructed faces from eyeglass-occluded photos should show visible wrinkles and folds instead of a smooth, glasses-flattened surface.
  • The same pipeline can handle multiple occlusion types such as glasses, hair, palms, food, and fingers simultaneously, because the 2D synthesis step regenerates a clean frontal face before shape regression.
  • Face verification on the reconstructed shapes degrades only slightly when glasses are present, indicating the occluder no longer corrupts the recovered identity shape.
  • Because coarse shape and detail are estimated separately, an error in local detail estimation does not destabilize the global face shape.
  • The bump-map representation keeps the added detail inexpensive, avoiding a full high-resolution mesh regression on every pixel.

Reading between the lines

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

  • Editorial inference: the quality ceiling of the whole pipeline is set by the 2D eyeglass-removal stage, so failures in face synthesis are likely to dominate errors in occluded regions regardless of the 3D regressor.
  • Editorial inference: the geometry-detail component is not independently reproducible until the authors disclose the source of the ground-truth bump maps used in the detail loss.
  • Editorial inference: the coarse-shape/detail decomposition suggests a drop-in extension in which any single-image shape regressor could be paired with the same bump-map refinement network.
  • Editorial inference: the reported verification-drop measure is an indirect proxy; a direct comparison against high-resolution 3D scans would be a stronger test of whether the added details are geometrically accurate.
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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 / 5 minor

Summary. The paper presents a two-stage pipeline for single-image 3D face reconstruction under occlusion. In the first stage, a face parsing network and a face edge-line network produce a parsing map and an edge map, which are combined using the authors' earlier algorithm [42] and fed with the input image to a face image synthesis network that removes eyeglasses. In the second stage, a ResNet-50 regresses a 239-dimensional vector of 3DMM-related coefficients, and a bump map is introduced to add mid-level geometry detail. The loss in Eq. (6) is meant to supervise this bump-map detail. Experiments consist of qualitative comparisons with Sela et al., PRNet, and 3DDFA, plus quantitative LFW face-verification tests of reconstructed shapes with and without eyeglasses.

Significance. If the proposed separation of global shape regression from bump-map detail prediction under occlusion were fully specified and validated, it would address a real gap: most single-image 3DMM methods either ignore mid-level geometric details or degrade under occlusion. The authors provide concrete weight values for the shape loss, compare against three baselines qualitatively, and report numerical LFW verification results rather than showing only subjective images. However, the central training signal for the claimed novelty is undefined, the estimated bump map has no described network or integration path, and the only quantitative table shows the proposed method below the cited baseline. No code or data are provided, so the manuscript as written is not reproducible and the headline contribution is not falsifiable.

major comments (4)
  1. [Section 3.3, Eq. (6)] The geometry-detail loss L_geo is defined as ||Φ̃ − Φ|| + ||∂Φ̃/∂x − ∂Φ/∂x|| + ||∂Φ̃/∂y − ∂Φ/∂y||, where Φ̃ is called the ground truth. The manuscript never states where Φ̃ comes from. The datasets listed in Section 4 (Helen, CelebAMask-HQ, 300W, AFLW) are 2D image, landmark, and segmentation datasets; none provides depth scans, displacement maps, or bump maps. Without a defined source of Φ̃, the gradient of Eq. (6) cannot be computed, so the claimed training of geometry details is undefined. This is an internal omission, not a disagreement with a baseline, and it makes the paper's central claim unfalsifiable as written.
  2. [Section 3.3, Eq. (5)] The definition of the bump map is tautological: Φ(b) is defined as φ(d′(b) − d(b)) and then inverted to obtain d′(b) = d(b) + φ^{-1}(Φ(b)). The text never describes a network or algorithm that predicts Φ from the regressed vector y ∈ R^239, nor how Φ is combined with the base shape S_base to produce the final detailed mesh. The phrase 'Inspired by the method of image-to-image translation' is not an implementation. Thus even if a ground-truth Φ̃ were available, the estimated Φ is unspecified and the pipeline cannot be reproduced.
  3. [Table 1] The quantitative evidence contradicts the claimed robustness. For LFW verification, the proposed method with glasses gives EER 84.37 ± 1.44, accuracy 85.79 ± 0.42, and nAUC 92.87 ± 1.09, all lower than Tran et al.'s 89.40 ± 1.52, 89.36 ± 1.25, and 95.90 ± 0.95. Even the no-glasses result (nAUC 95.37 ± 0.65) is slightly below Tran et al. The text states that the limited drop 'demonstrating the robustness of our method,' but the numbers show the reconstruction is less discriminative than the baseline, so the quantitative claims of state-of-the-art robustness are not supported by the presented evidence.
  4. [Sections 3.1, 3.3, and reference [42]] The final face parsing map M_fin is obtained following 'Zhao et al.'s Algorithm [42],' which is the authors' own prior work. The algorithm is not summarized or independently validated in this manuscript. Because the eyeglass-removal step and the subsequent reconstruction depend on M_fin, a reader cannot evaluate whether the proposed pipeline, as opposed to the prior work, is responsible for the occlusion handling. At minimum, the dependency should be described or the component ablated.
minor comments (5)
  1. [Section 3.3] The text says the ResNet 'was trained under weakly supervised' and later says 'we used a fixed ResNet-50 network to regress these coefficients'; please clarify whether the regressor is fixed after a separate training stage or jointly optimized.
  2. [Eq. (1)] The symbol d_gt is used both as a scalar 0/1 condition and as a 'ground-truth distance matrix'; please separate the thresholding condition from the distance map notation.
  3. [Section 3.3] The sentence 'we found that by adding these last two terms ... reduce bump map noise' is presented without experimental support; an ablation of the two gradient terms in Eq. (6) should be added.
  4. [Section 3.2] The number of U-Net stacks M and the message-passing area partition are never specified; please provide concrete architecture details so the method can be reimplemented.
  5. [Throughout] There are several presentation issues: 'Vetoer's first 3D face' should be 'Vetter'; reference [31] is attributed to 'Gusi et al.' in Section 3.1 but to 'Te et al.' in Section 4; and the abstract's claim of 'state-of-the-art qualitative performance' should be reconciled with the less favorable quantitative results in Table 1.

Circularity Check

2 steps flagged · score 3.0 of 10

Mild circularity: Eq. (1) defines the discriminator ground truth d_gt via itself and the final parsing map relies on the authors' own prior algorithm [42]; the central bump-map detail reconstruction is otherwise independent.

  1. self definitional [Section 3.2, Eq. (1)]
    "In order to determine whether a generated edge line map is fake or not, the ground truthdgt can be calculated as: dgt(Medge, Scoor) = ( 0, Ests∈Scoor (dgt < θ) < δ 1,other cases (1)"

    The quantity d_gt being defined appears inside its own defining condition: d_gt(M_edge,S_coor) is 0 when Est_{s∈S_coor}(d_gt < θ) < δ, and 1 otherwise. Since d_gt is not defined before this equation, the right-hand side cannot be evaluated; as written, this is a self-referential definition. This d_gt is then used in Eq. (2) as the target for the discriminator D, so the adversarial training signal for the edge-line map depends on an ill-defined quantity. The issue is auxiliary rather than central, but it is a concrete circular definition in the pipeline.

  2. self citation load bearing [Section 3.3, first paragraph (also Fig. 1 caption)]
    "Then we obtain the final face parsing mapMfin following Zhaoet al.'s Algorithm [42]."

    The final face parsing map M_fin, which drives eyeglasses removal and the subsequent 3D reconstruction, is obtained exclusively by invoking the authors' own prior algorithm [42]. The present paper gives no description, training details, or independent validation of that algorithm, so the occlusion-handling part of the pipeline rests on a same-author citation. This is load-bearing but not a logical reduction: [42] is a separately published component, and the paper's geometry-detail claim does not reduce to it.

full rationale

The central derivation is not circular: the method represents geometry details as a bump map Φ defined as the encoded depth offset between the detailed shape and the base shape, and the detailed depth is recovered as d'(b) = d(b) + φ^{-1}(Φ(b)). The training loss in Eq. (6) compares a predicted bump map with a ground-truth bump map; no fitted quantity is renamed as a prediction, and the LFW evaluation is an external benchmark against independent baselines. Two issues raise the score above zero, but neither makes the core claim equivalent to its input. First, Eq. (1) defines the discriminator ground-truth label d_gt(M_edge,S_coor) using the same symbol d_gt inside its own condition, which is a self-referential definition as written; the label feeds Eq. (2) and thus the edge-line adversarial training. Second, the final face parsing map M_fin is obtained by the authors' own prior algorithm [42] with no in-paper description or validation; M_fin is load-bearing for occlusion handling. This is a mild same-author dependency rather than a circular derivation, because [42] is an external publication and the geometry-detail contribution stands apart from it. The most serious manuscript defect is not circularity at all: the ground-truth bump map Φ̃ in Eq. (6) has no stated source, and the paper never specifies which network produces Φ from the regressed vector y ∈ R^239. That missing supervision is a reproducibility gap, but it is an omission, not a circular step. Overall score 3: some self-referential and self-citation load, but the central claim still has independent content.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a training signal (ground truth bump map) that is never described, on the fidelity of an existing inpainting network to remove glasses, and on the validity of the 3DMM basis. The paper's own prior algorithm [42] is used to derive the parsing map, adding a self-citation dependency.

free parameters (8)
  • λ_feat = 0.2
    Hand-set weight for feature loss in Eq. (4).
  • λ_regu = 3.6e-4
    Hand-set regularization weight in Eq. (4).
  • λ_phot = 1.4
    Hand-set photometric loss weight in Eq. (4).
  • λ_land = 1.6e-3
    Hand-set landmark loss weight in Eq. (4).
  • θ
    Distance threshold in edge-line effectiveness Eq. (1); value not reported.
  • δ
    Probability threshold in Eq. (1); value not reported.
  • M
    Number of message-passing areas in the edge-lines module; not specified.
  • φ(·) = linear [0,255] encoding
    Bump map encoding function in Eq. (5), assumed invertible.
assumptions (5)
  • ad hoc to paper Ground truth bump maps Φ̃ exist and are accessible for training
    Eq. (6) requires Φ̃, but no source is described; this is load-bearing for the geometry detail claim.
  • domain assumption The face synthesis network (MaskGAN [15]) removes eyeglasses without artifacts
    The cleaned image I_out is the only input to the 3D regressor; artifacts would corrupt the reconstruction.
  • domain assumption The 3DMM shape and expression basis represent global face shape sufficiently
    The bump map is added to a 3DMM base shape; if the base shape is poor, details cannot fix global error.
  • ad hoc to paper The final face parsing map M_fin from Zhao et al. [42] is correct
    The pipeline depends on the authors' own prior parsing algorithm without independent validation.
  • standard math The bump map encoding φ is invertible
    Eq. (5) uses φ^{-1} to recover depth differences.

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Cite this review

Pith. "Pith review of 3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes." pith.science (2026). https://pith.science/paper/B6NKITAZ

@misc{pith2026241219849,
  author       = {Pith},
  title        = {Pith review of: 3D Face Reconstruction With Geometry Details From a Single Color Image Under Occluded Scenes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B6NKITAZ}},
  note         = {Machine review of arXiv:2412.19849}
}
read the original abstract

3D face reconstruction technology aims to generate a face stereo model naturally and realistically. Previous deep face reconstruction approaches are typically designed to generate convincing textures and cannot generalize well to multiple occluded scenarios simultaneously. By introducing bump mapping, we successfully added mid-level details to coarse 3D faces. More innovatively, our method takes into account occlusion scenarios. Thus on top of common 3D face reconstruction approaches, we in this paper propose a unified framework to handle multiple types of obstruction simultaneously (e.g., hair, palms and glasses et al.).Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with geometry details from captured facial images under occluded scenes.

Figures

Figures reproduced from arXiv: 2412.19849 by the authors.

Figure 1
Figure 1. Method overview. At first, as input for our face image synthesis network, we need the target image Iin and map Mfin. We utilize the face parsing map generation module and edge lines map generation module to obtain the map Mfa and Medge. Then we obtain the final face parsing map Mfin following Zhao et al.’s Algorithm [42]. After obtaining the face image Iout with eyeglasses removed, in step two, we leverage ResNet-50… view at source ↗
Figure 2
Figure 2. The overview of the proposed face parsing network. network Nfa as the backbone frame for face parsing. We take Nfa to extract features at different levels for multi-scale illustration. In the structure of Nfa, high-level features contain semantic information while low-level features show local details, both of which are essential for face parsing. We feed the feature map with multi-scale information into the Edge Aw… view at source ↗
Figure 3
Figure 3. The overview of the proposed face edge lines map generation approach. In order to generate an accurate face parsing map, our method uses face edge lines to guide the reconstruction of the face parsing map. Face edge lines is closely related to the facial landmark. The reason why we choose face edge lines instead of landmarks is that landmarks have difficulties in presenting the accurate facial features structure [37… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Comparison of qualitative results. Baseline methods from left to right: Sela et al., PRNet,3DDFA and our method. clearly shows the outline of the eyeglasses. Their failure may be due to more focus on local details, which weakly regularizes the global shape. However, ou…
Figure 5
Figure 5. Figure 5: Reconstructions with eyeglasses. Left: Qualitative results of Sela et al. [29] and our shape. Right: LFW verification ROC for the shapes, with and without eyeglasses [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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Reference graph

Works this paper leans on

42 extracted references · 26 canonical work pages

  1. [15]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Lee, C.H., Liu, Z., Wu, L., Luo, P.: Maskgan: Towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5549–5558 (2020)

    2020

  2. [42]

    In: Computer Graphics International Conference

    Zhao, D., Qi, Y.: Generative face parsing map guided 3d face reconstruction under occluded scenes. In: Computer Graphics International Conference. Springer (2021)

    work page 2021

  3. [1]

    In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition

    Abrevaya, V.F., Boukhayma, A., Torr, P.H., Boyer, E.: Cross-modal deep face nor- mals with deactivable skip connections. In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition. pp. 4979–4989 (2020)

    work page 2020

  4. [2]

    In: Siggraph

    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: Siggraph. vol. 99, pp. 187–194 (1999)

    1999

  5. [3]

    In: Proceedings of the Asian Conference on Computer Vision (2020)

    Cheng, S., Tzimiropoulos, G., Shen, J., Pantic, M.: Faster, better and more de- tailed: 3d face reconstruction with graph convolutional networks. In: Proceedings of the Asian Conference on Computer Vision (2020)

    work page 2020

  6. [4]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    Chu, X., Ouyang, W., Li, H., Wang, X.: Structured feature learning for pose esti- mation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4715–4723 (2016)

    work page 2016

  7. [5]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops

    Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3d face recon- struction with weakly-supervised learning: From single image to image set. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 0–0 (2019)

    work page 2019

  8. [6]

    In: Proceedings of the European Conference on Computer Vision (ECCV)

    Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3d face reconstruction and dense alignment with position map regression network. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 534–551 (2018)

    2018

Show all 42 references
  1. [7]

    arXiv preprint arXiv:2009.09960 (2020)

    Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., Li, S.Z.: Towards fast, accurate and stable 3d dense face alignment. arXiv preprint arXiv:2009.09960 (2020)

    2020 arXiv

  2. [8]

    In: Thirty- Second AAAI Conference on Artificial Intelligence (2018) Title Suppressed Due to Excessive Length 11

    Guo, T., Kim, Y., Zhang, H., Qian, D., Yoo, B., Xu, J., Zou, D., Han, J.J., Choi, C.: Residual encoder decoder network and adaptive prior for face parsing. In: Thirty- Second AAAI Conference on Artificial Intelligence (2018) Title Suppressed Due to Excessive Length 11

    2018

  3. [9]

    IEEE transactions on pattern analysis and machine intelligence41(6), 1294–1307 (2018)

    Guo,Y.,Cai,J.,Jiang,B.,Zheng,J.:Cnn-basedreal-timedensefacereconstruction with inverse-rendered photo-realistic face images. IEEE transactions on pattern analysis and machine intelligence41(6), 1294–1307 (2018)

    2018

  4. [10]

    Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A databaseforstudyingfacerecognitioninunconstrainedenvironments.In:Workshop on faces in’Real-Life’Images: detection, alignment, and recognition (2008)

    2008

  5. [11]

    IEEE transactions on pattern analysis and machine intelligence 33(2), 394–405 (2010)

    Kemelmacher-Shlizerman, I., Basri, R.: 3d face reconstruction from a single image using a single reference face shape. IEEE transactions on pattern analysis and machine intelligence 33(2), 394–405 (2010)

    2010

  6. [12]

    arXiv preprint arXiv:1412.6980 (2014)

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

    2014 arXiv

  7. [13]

    In: 2011 IEEE international conference on computer vision workshops (ICCV work- shops)

    Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In: 2011 IEEE international conference on computer vision workshops (ICCV work- shops). pp. 2144–2151. IEEE (2011)

    2011

  8. [14]

    In: European conference on computer vision

    Le, V., Brandt, J., Lin, Z., Bourdev, L., Huang, T.S.: Interactive facial feature localization. In: European conference on computer vision. pp. 679–692. Springer (2012)

    2012

  9. [16]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Lee, G.H., Lee, S.W.: Uncertainty-aware mesh decoder for high fidelity 3d face reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6100–6109 (2020)

    2020

  10. [17]

    In: 2020 25th International Conference on Pattern Recognition (ICPR)

    Li, K., Wu, H., Shang, X., Shen, Z., Xiong, G., Dong, X., Hu, B., Wang, F.Y.: Joint face alignment and 3d face reconstruction with efficient convolution neural networks. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 6973–6979. IEEE (2021)

    2021

  11. [18]

    In: 2020 25th International Conference on Pattern Recognition (ICPR)

    Li, X., Wu, S.: Multi-attribute regression network for face reconstruction. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 7226–7233. IEEE (2021)

    2021

  12. [19]

    In: Proceedings of the asian conference on computer vision (2020)

    Liu, P., Han, X., Lyu, M., King, I., Xu, J.: Learning 3d face reconstruction with a pose guidance network. In: Proceedings of the asian conference on computer vision (2020)

    2020

  13. [20]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Masi, I., Mathai, J., AbdAlmageed, W.: Towards learning structure via consensus for face segmentation and parsing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5508–5518 (2020)

    2020

  14. [21]

    arXiv preprint arXiv:1901.00212 (2019)

    Nazeri, K., Ng, E., Joseph, T., Qureshi, F.Z., Ebrahimi, M.: Edgeconnect: Generative image inpainting with adversarial edge learning. arXiv preprint arXiv:1901.00212 (2019)

    2019 arXiv

  15. [22]

    In: European conference on computer vision

    Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose esti- mation. In: European conference on computer vision. pp. 483–499. Springer (2016)

    2016

  16. [23]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context en- coders: Feature learning by inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2536–2544 (2016)

    2016

  17. [24]

    In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques

    Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environ- ment maps. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. pp. 497–500 (2001)

    2001

  18. [25]

    In: 2016 Fourth International Conference on 3D Vision (3DV)

    Richardson, E., Sela, M., Kimmel, R.: 3d face reconstruction by learning from synthetic data. In: 2016 Fourth International Conference on 3D Vision (3DV). pp. 460–469. IEEE (2016) 12 Dapeng Zhao and Yue Qi

    2016

  19. [26]

    In: International Conference on Medical image computing and computer-assisted intervention

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedi- cal image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

    2015

  20. [27]

    In: Proceedings of the IEEE International Conference on Computer Vision Workshops

    Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild chal- lenge: The first facial landmark localization challenge. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 397–403 (2013)

    2013

  21. [28]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Sangkloy, P., Lu, J., Fang, C., Yu, F., Hays, J.: Scribbler: Controlling deep im- age synthesis with sketch and color. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5400–5409 (2017)

    2017

  22. [29]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Sela, M., Richardson, E., Kimmel, R.: Unrestricted facial geometry reconstruc- tion using image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1576–1585 (2017)

    2017

  23. [30]

    arXiv preprint arXiv:2007.12494 (2020)

    Shang, J., Shen, T., Li, S., Zhou, L., Zhen, M., Fang, T., Quan, L.: Self-supervised monocular 3d face reconstruction by occlusion-aware multi-view geometry consis- tency. arXiv preprint arXiv:2007.12494 (2020)

    2020 arXiv

  24. [31]

    In: European Conference on Computer Vision

    Te, G., Liu, Y., Hu, W., Shi, H., Mei, T.: Edge-aware graph representation learning and reasoning for face parsing. In: European Conference on Computer Vision. pp. 258–274. Springer (2020)

    2020

  25. [32]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2face: Real-time face capture and reenactment of rgb videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2387–2395 (2016)

    2016

  26. [33]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    Tuan Tran, A., Hassner, T., Masi, I., Medioni, G.: Regressing robust and discrim- inative 3d morphable models with a very deep neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5163–5172 (2017)

    2017

  27. [34]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    Tuan Tran, A., Hassner, T., Masi, I., Paz, E., Nirkin, Y., Medioni, G.: Extreme 3d face reconstruction: Seeing through occlusions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3935–3944 (2018)

    2018

  28. [35]

    Springer (1998)

    Vetter, T., Blanz, V.: Estimating coloured 3d face models from single images: An examplebasedapproach.In:Europeanconferenceoncomputervision.pp.499–513. Springer (1998)

    1998

  29. [36]

    IEEE Transactions on Image Processing28(9), 4659–4670 (2019)

    Wei, Z., Liu, S., Sun, Y., Ling, H.: Accurate facial image parsing at real-time speed. IEEE Transactions on Image Processing28(9), 4659–4670 (2019)

    2019

  30. [37]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Wu, W., Qian, C., Yang, S., Wang, Q., Cai, Y., Zhou, Q.: Look at boundary: A boundary-aware face alignment algorithm. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2129–2138 (2018)

    2018

  31. [38]

    Neurocomputing 358, 294–308 (2019)

    Ye, L., Zhang, B., Yang, M., Lian, W.: Triple-translation gan with multi-layer sparse representation for face image synthesis. Neurocomputing 358, 294–308 (2019)

    2019

  32. [39]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Zhan, F., Zhu, H., Lu, S.: Spatial fusion gan for image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3653–3662 (2019)

    2019

  33. [40]

    In: IEEE International Conference on Multimedia and Expo (ICME)

    Zhang, Y., Zhang, H., Wu, G., Li, J.: Spatio-temporal self-supervision enhanced transformer networks for action recognition. In: IEEE International Conference on Multimedia and Expo (ICME). IEEE (2022)

    2022

  34. [41]

    In: 2022 26th International Conference on Pattern Recognition (ICPR)

    Zhang, Y., Zhang, H., Wu, G., Xu, Y., Shi, Z., Li, J.: Tmn: Temporal-guided mul- tiattention network for action recognition. In: 2022 26th International Conference on Pattern Recognition (ICPR). IEEE

    2022

Pith tools

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