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REVIEW 3 major objections 4 minor 35 references

Monocular 3D Hand Pose Estimation with Implicit Camera Alignment

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that 3D hand pose can be extracted from a single RGB image using only 2D keypoints, with a rigid-alignment fitting step that removes the need for camera parameters.

desk verdict A clean, incremental optimization pipeline for 3D hand pose from MediaPipe 2D keypoints; the camera-agnostic claim is plausible but the unspecified pixel-to-camera transform in Sec. 5 leaves the headline AUC numbers unverifiable. read the letter →

arxiv 2506.11133 v2 pith:7C5UEX7E submitted 2025-06-10 cs.CV cs.GRcs.LGeess.IV

Christos Pantazopoulos , Spyridon Thermos , Gerasimos Potamianos This is my paper
classification cs.CVcs.GRcs.LGeess.IV
keywords 3DhandposeestimationmonocularRGBMANOmodelMediaPipelandmarkscamera-agnosticoptimization-basedfittingfingertiplossrigidalignment
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

Monocular 3D hand pose estimation usually needs camera intrinsics to lift 2D observations into 3D, or a large training set. This paper claims that neither is necessary: taking the 21 hand keypoints detected by MediaPipe, a two-stage optimization pipeline fits the MANO parametric hand model to those keypoints using a rigid alignment step and a weighted fingertip loss, and in doing so absorbs the camera-dependent scale and orientation implicitly. On the EgoDexter and Dexter+Object benchmarks the pipeline reaches AUC of PCK 0.883 and 0.946 respectively, on par with or better than state-of-the-art learning-based and optimization-based methods. If the claim is right, AR, VR, human-computer interaction, and robotics can recover metric 3D hand poses from ordinary RGB images without calibrating the camera.

What carries the argument

The load-bearing machinery is the MANO hand model (45 pose parameters in axis-angle form, 10 shape parameters, 3 global-orientation parameters) fitted to MediaPipe's 21 2D hand keypoints. The alignment step that replaces camera calibration is a 4x4 rigid transformation built from the six palm joints [0, 1, 5, 9, 13, 17] and a scale factor computed from the wrist-to-index-MCP distance, which is applied before optimization and inverted afterward. The refinement stage minimizes MSE, Geman-McClure, or Huber losses, with a variant that weights fingertip keypoints more heavily; L-BFGS/BFGS quasi-Newton optimizers perform the unconstrained optimization, and anatomical joint limits are added only in the two-stage variant.

What would settle it

Render a hand with a known camera focal length, run MediaPipe to get 2D keypoints, then run the pipeline at two very different assumed focal lengths while keeping the 2D keypoints identical; if the recovered 3D keypoint positions in millimeters change materially, then camera information is entering through the coordinate conversion rather than being avoided.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes a camera-agnostic route to 3D hand articulation: instead of regressing 3D pose from images or estimating camera parameters, it treats the problem as fitting MANO to 2D detections. A rigid transformation computed from six stable palm joints (wrist, thumb base, index base, middle base, ring base, and pinky base keypoints 0, 1, 5, 9, 13, 17) plus a scale from the wrist-to-index-MCP distance aligns the neutral MANO hand to the MediaPipe keypoints; after optimization the transform is inverted to recover pose in the original coordinate system. A weighted loss that up-weights fingertip keypoints fixes the tendency of dense palm regions to dominate, and anatomical joint-limit constraints are tried but ultimately hurt performance (experiment H). The paper reports best results with L-BFGS/BFGS and weighted MSE, Geman-McClure, or Huber losses, yielding AUC of PCK 0.883 on EgoDexter and 0.946 on Dexter+Object, and shows the method operating on the Mona Lisa painting as an in-the-wild demonstration.

Load-bearing premise

The method assumes its alignment step fixes the hand's size and depth well enough to give real millimeter positions, and that the coordinate conversion used for evaluation does not quietly supply camera parameters or ground-truth depth; if either fails, the camera-agnostic claim and its accuracy numbers are unsupported.

Editorial extensions

If this is right

  • 3D hand pose can be recovered from a single RGB image with no camera calibration, so the method applies to arbitrary in-the-wild images such as archived photographs or paintings.
  • Because the method is optimization-based and uses no training, it is directly applicable to new datasets and hands without collecting annotations.
  • The best configurations land within 0.002 AUC of the best learning-based method on Dexter+Object and outperform it on EgoDexter, suggesting calibration-free fitting is a viable alternative to trained regressors.
  • The failure of the anatomical two-stage variant (experiment H, AUC 0.492 on EgoDexter) implies that over-constrained joint limits can degrade, rather than regularize, an already reliable fit.
  • The method inherits its accuracy from the 2D detector: the paper identifies sensitivity to 2D keypoint quality despite the use of hand priors.

Reading between the lines

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

  • Editorial inference: if MediaPipe's z channel encodes depth learned from training data, the pipeline is implicitly consuming a depth prior; the 'no camera parameters' property should be read as 'no explicit calibration,' not 'no depth signal.'
  • Editorial inference: since the scale is taken from a wrist-to-knuckle distance of the MANO template, absolute millimeter accuracy likely depends on how close the true hand size is to the template; a person with a much larger or smaller hand would shift the EPE even with perfect articulation.
  • Editorial inference: the method could be tested as a seed or initialization for learning-based methods, or extended to track hands across video by using the previous frame's pose as initialization instead of the neutral pose.
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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

3 major / 4 minor

Summary. The paper proposes an optimization-based pipeline for monocular 3D hand pose estimation that takes MediaPipe 2D hand keypoints as input, performs a rigid alignment plus a wrist-to-MCP scale factor to initialize a MANO model, and then refines the fit with MSE, Geman-McClure, Huber, or anatomically constrained losses. The authors claim that this procedure avoids any need to know or estimate camera parameters, and they report competitive state-of-the-art results on EgoDexter and Dexter+Object (AUC of PCK 0.883 and 0.946, respectively, in Table 3). The paper includes an ablation study over optimizer/loss configurations, qualitative in-the-wild results, and a link to code.

Significance. If the central claim is valid, the paper would make a useful practical contribution: a training-free, camera-agnostic method that turns existing 2D hand keypoint detectors into 3D MANO pose estimates with accuracy competitive with learned methods. The optimization pipeline is clearly structured, the ablations are internally consistent, and the authors provide code and qualitative evidence of generalization to unconstrained images. However, the quantitative comparison to state of the art rests entirely on an underspecified conversion from the method's pixel-space output to the millimeter-scale camera-coordinate ground truth, and the reported single AUC values lack any indication of variance or the exact frame set used. These issues must be resolved before the headline claims can be accepted.

major comments (3)
  1. [Sec. 5, Datasets] The paragraph 'However, our method ... outputs 21 keypoints in pixel space in (x, y, z) format, including depth values. Thus, we must transform them to camera coordinates' never specifies what this transform is. This is load-bearing because Section 4 computes the scale factor from pixel-space wrist-to-MCP distances, so the optimization output is in pixel units and cannot be compared with millimeter ground truth without external metric information. Please give the exact formula or procedure for this transform and state explicitly whether it uses camera intrinsics, ground-truth depth, ground-truth scale, or any other dataset-derived quantity. If it does, then the 'camera-agnostic' claim and the quantitative results in Tables 2 and 3 are not supported as stated.
  2. [Sec. 5.2, Table 3 and Sec. 5.3] The evaluation frame set is not defined. Section 5.3 states that in the failure case of Fig. 5e, MediaPipe fails to detect a hand, and that 'EgoDexter does not provide ground-truth keypoints for such cases, making evaluation difficult.' If frames with failed MediaPipe detections are excluded from the quantitative evaluation, then the reported AUC/EPE values are conditional on successful detection and are not directly comparable to methods that are evaluated on all annotated frames. Please state the exact frame-selection criterion, report how many frames are used for each dataset, and provide variance estimates across runs and across MediaPipe versions, since only single values are reported.
  3. [Sec. 4, Optimization Pipeline and Table 1] The final optimization objective is never written down. The text mentions several loss functions and stages, and Table 1 lists which are active, but the actual objective (the weighting of each loss term, the fingertip weighting scheme, and the anatomical constraint weight) is not specified. This makes the method difficult to reproduce from the paper alone, even with the code link. Please include the full objective function and all weight values in the revised manuscript.
minor comments (4)
  1. [Abstract and Sec. 5.1] The abstract and contributions highlight anatomical constraints as part of the method, but Experiment H in Table 2, which is the only configuration using anatomical constraints, performs dramatically worse (AUC 0.492 vs. 0.883 on EgoDexter). The text should reconcile this discrepancy, either by removing anatomical constraints from the contribution list or by explaining why they are considered a positive component despite the negative ablation result.
  2. [Sec. 3, Rigid Transformations] The definition of a rigid transformation says it 'optionally' includes scaling, which is not the standard definition. The pipeline uses a similarity transform (rotation, translation, and uniform scale), so the terminology should be corrected.
  3. [Sec. 5.1] The sentence mentioning 'depth-related errors caused by MediaPipe's 3D predictions' suggests that MediaPipe's z-coordinate is used somewhere in the pipeline, but Section 4 describes fitting to 2D keypoints only. Please clarify how (or whether) the MediaPipe z-coordinate enters the optimization and evaluation.
  4. [References] Reference [10] is a Towards Data Science blog post; please consider replacing it with a peer-reviewed source for the 21-keypoint hand annotation convention.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the MANO-to-2D-keypoints fitting chain is self-contained; the unspecified pixel-to-camera evaluation transform is a correctness gap, not a circular derivation.

full rationale

The paper's derivation chain is self-contained. Input is MediaPipe 2D keypoints; Sec. 4 fits MANO by a rigid alignment (six palm joints, wrist-to-MCP scale) followed by optimization of MANO parameters under MSE/GM/Huber losses with fingertip weighting. No benchmark ground truth or camera intrinsics enter the optimization, and no parameter is fitted to the target quantity (3D GT pose) and then reported as a prediction. The principal quantitative claim is compared against SotA via the evaluation pipeline of [11] and [33]; the only flagged gap is Sec. 5's sentence 'Thus, we must transform them to camera coordinates to ensure compatibility with the dataset’s ground truth values,' where the transform is not specified. That omission is a real correctness/coordinate-frame risk (if the transform used GT depth or intrinsics, the camera-agnostic claim would be weakened), but it is an evaluation-protocol concern, not a circular step: the predicted 3D articulation is not defined in terms of the evaluation ground truth, and no self-citation chain or ansatz-imported assumption is load-bearing. Hence score 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or model entities; 'implicit camera alignment' is a procedural label, not an entity. The main hidden assumptions are the heuristic scale recovery and the unspecified evaluation transform to camera coordinates.

free parameters (3)
  • per-image scale factor s = not reported; ratio of wrist-to-MCP (keypoints 0-5) distances in image space vs MANO
    Used in Sec. 4 to set the initial 3D scale without camera intrinsics; if this heuristic does not approximate true metric scale, the mm-scale EPE/AUC comparison is invalid.
  • fingertip loss weights = not reported
    The paper states higher importance is given to fingertip keypoints (Sec. 4); exact weights are not given, and the ablation shows weighted vs unweighted variants differ (IDs A vs B in Table 2).
  • robust loss parameters (rho for Geman-McClure, delta for Huber) = not reported
    Custom implementations are described but parameter values are omitted (Sec. 5, Implementation Details); they affect optimization behavior in experiments C-F.
assumptions (4)
  • domain assumption MANO joints obtained via MANOTorch correspond to MediaPipe's 21 hand keypoints through linear interpolation.
    Sec. 4 states MANO keypoints are obtained through linear interpolation; this joint-to-joint correspondence is assumed without validation.
  • domain assumption MediaPipe's 2D keypoints and z-coordinate estimates are sufficiently accurate for the optimization to converge to a valid 3D pose.
    The method's input is entirely MediaPipe detections; the paper itself concludes that 2D keypoint accuracy is the limiting factor (Abstract, Sec. 6).
  • ad hoc to paper The monocular 3D scale can be recovered from a 2D rigid alignment plus a wrist-to-MCP scale factor.
    This is the paper's implicit camera alignment mechanism (Sec. 4); it is not derived from projective geometry and is the paper's own assumption to make the camera-agnostic claim work.
  • domain assumption The evaluation protocol of [11] and [33], including AUC over 20-50 mm PCK, transfers directly to an optimization method without a training phase.
    Sec. 5 states they follow the evaluation pipeline of [11] and [33], but the specifics of coordinate conversion are not given.

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Cite this review

Pith. "Pith review of Monocular 3D Hand Pose Estimation with Implicit Camera Alignment." pith.science (2026). https://pith.science/paper/7C5UEX7E

@misc{pith2026250611133,
  author       = {Pith},
  title        = {Pith review of: Monocular 3D Hand Pose Estimation with Implicit Camera Alignment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7C5UEX7E}},
  note         = {Machine review of arXiv:2506.11133}
}
read the original abstract

Estimating the 3D hand articulation from a single color image is an important problem with applications in Augmented Reality (AR), Virtual Reality (VR), Human-Computer Interaction (HCI), and robotics. Apart from the absence of depth information, occlusions, articulation complexity, and the need for camera parameters knowledge pose additional challenges. In this work, we propose an optimization pipeline for estimating the 3D hand articulation from 2D keypoint input, which includes a keypoint alignment step and a fingertip loss to overcome the need to know or estimate the camera parameters. We evaluate our approach on the EgoDexter and Dexter+Object benchmarks to showcase that it performs competitively with the state-of-the-art, while also demonstrating its robustness when processing "in-the-wild" images without any prior camera knowledge. Our quantitative analysis highlights the sensitivity of the 2D keypoint estimation accuracy, despite the use of hand priors. Code is available at the project page https://cpantazop.github.io/HandRepo/

Figures

Figures reproduced from arXiv: 2506.11133 by the authors.

Figure 1
Figure 1. The 21 hand keypoints estimated by MediaPipe [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Our proposed pipeline. 4. Method Overview. To tackle the problem of fitting an artic￾ulated 3D hand from a single RGB image, we design the pipeline shown in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. 3D PCK evaluation results. the weighted fingertips loss may be attributed to the spe￾cific evaluation datasets focusing primarily on fingertip key￾points. Additionally, we can observe that in experiment H, this two-stage approach integrating anatomical constraints resulted in worse overall performance although it success￾fully corrected depth-related errors caused by MediaPipe’s 3D predictions in specific cases. Ove… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Qualitative results on both EgoDexter and Dexter+Object datasets as well as images “in the wild.” Examples include successful [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Works this paper leans on

35 extracted references · 34 canonical work pages

  1. [1]

    https : / / developers

    Hand landmarks detection guide. https : / / developers . google . com / mediapipe / solutions/vision/hand_landmarker [Accessed: (10/6/2025)]. 1, 2, 3, 4

    work page 2025

  2. [2]

    https://github.com/lixiny/manotorch [Ac- cessed: (10/6/2025)]

    Manotorch: framework implementing MANO in PyTorch. https://github.com/lixiny/manotorch [Ac- cessed: (10/6/2025)]. 3, 5

    work page 2025

  3. [3]

    https: //www.probabilitycourse.com/chapter9/9_ 1 _ 5 _ mean _ squared _ error _ MSE

    Mean squared error (MSE), probabilitycourse.com. https: //www.probabilitycourse.com/chapter9/9_ 1 _ 5 _ mean _ squared _ error _ MSE . php[Accessed: (9/2/2025)]. 3

    work page 2025

  4. [4]

    https://mmpose

    MMPose hand keypoint estimation. https://mmpose. readthedocs.io/en/latest/demos.html [Ac- cessed: (10/6/2025)]. 3

    work page 2025

  5. [5]

    https : / / github

    OpenPose. https : / / github . com / CMU - Perceptual - Computing - Lab / openpose [Ac- cessed: (10/6/2025)]. 3

    work page 2025

  6. [6]

    https : / / medium

    Rigid transformation in 3D space: Translation and rotation. https : / / medium . com / @parkie0517 / rigid - transformation- in- 3d- space- translation- and - rotation - d701d8859ba8 [Accessed: (10/6/2025)]. 3

    work page 2025

  7. [7]

    minimize

    scipy.optimize. minimize. https : / / docs . scipy . org/doc/scipy/reference/generated/scipy. optimize.minimize.html [Accessed: (10/6/2025)]. 4, 5

    work page 2025

  8. [8]

    Push- ing the envelope for RGB-based dense 3D hand pose esti- mation via neural rendering

    Seungryul Baek, Kwang In Kim, and Tae-Kyun Kim. Push- ing the envelope for RGB-based dense 3D hand pose esti- mation via neural rendering. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1067–1076, 2019. 2, 7

    work page 2019

Show all 35 references
  1. [9]

    Adnane Boukhayma, Rodrigo de Bem, and Philip H. S. Torr. 3D hand shape and pose from images in the wild. 2019 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 10835–10844, 2019. 2, 7

    2019

  2. [10]

    Gentle introduction to 2D hand pose estimation: Approach explained, 2021

    Olga Chernytska. Gentle introduction to 2D hand pose estimation: Approach explained, 2021. https : / / towardsdatascience . com / gentle - introduction - to - 2d - hand - pose - estimation - approach - explained - 4348d6d79b11 [Accessed: (10/6/2025)]. 2

    2021

  3. [11]

    Drosakis Drosakis and Antonis A. Argyros. 3D hand shape and pose estimation based on 2D hand keypoints. Proceed- ings of the 16th International Conference on PErvasive Tech- nologies Related to Assistive Environments, 2023. 2, 3, 5, 7

    2023

  4. [12]

    3D hand shape and pose estimation from a single RGB image

    Liuhao Ge, Zhou Ren, Yuncheng Li, Zehao Xue, Yingying Wang, Jianfei Cai, and Junsong Yuan. 3D hand shape and pose estimation from a single RGB image. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10825–10834, 2019. 2

    2019

  5. [13]

    Stuart Geman and Donald E. McClure. Statistical methods for tomographic image reconstruction. Bulletin of the Inter- national Statistical Institute, 52(4):5–21, 1987. 3

    1987

  6. [14]

    Peter J. Huber. Robust estimation of a location parameter. Annals of Mathematical Statistics, 35:492–518, 1964. 3 8

    1964

  7. [15]

    Breuel, Juergen Gall, and Jan Kautz

    Umar Iqbal, Pavlo Molchanov, Thomas M. Breuel, Juergen Gall, and Jan Kautz. Hand pose estimation via latent 2.5D heatmap regression. In European Conference on Computer Vision, 2018. 2, 7

    2018

  8. [16]

    Bronstein, and Stefanos Zafeiriou

    Dominik Kulon, Riza Alp G ¨uler, Iasonas Kokkinos, Michael M. Bronstein, and Stefanos Zafeiriou. Weakly- supervised mesh-convolutional hand reconstruction in the wild. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4989–4999, 2020. 2

    2020

  9. [17]

    Mo- bileHand: Real-time 3D hand shape and pose estimation from color image

    Guan Ming Lim, Prayook Jatesiktat, and Wei Tech Ang. Mo- bileHand: Real-time 3D hand shape and pose estimation from color image. In International Conference on Neural Information Processing, 2020. 2

    2020

  10. [18]

    John Lin, Ying Wu, and T.S. Huang. Modeling the con- straints of human hand motion. In Proceedings Workshop on Human Motion, pages 121–126, 2000. 3

    2000

  11. [19]

    MediaPipe: A framework for building perception pipelines

    Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris Mc- Clanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo- Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. MediaPipe: A framework for building perception pipelines. ArXiv...

    1906 arXiv

  12. [20]

    Ganerated hands for real-time 3D hand tracking from monocular RGB

    Franziska Mueller, Florian Bernard, Oleksandr Sotny- chenko, Dushyant Mehta, Srinath Sridhar, Dan Casas, and Christian Theobalt. Ganerated hands for real-time 3D hand tracking from monocular RGB. In Proceedings of Computer Vision and Pattern Recognition (CVPR), 2018. 2, 7

    2018

  13. [21]

    Real-time hand tracking under occlusion from an egocentric RGB-D sensor

    Franziska Mueller, Dushyant Mehta, Oleksandr Sotny- chenko, Srinath Sridhar, Dan Casas, and Christian Theobalt. Real-time hand tracking under occlusion from an egocentric RGB-D sensor. In Proceedings of International Conference on Computer Vision (ICCV), 2017. 6

    2017

  14. [22]

    Updating Quasi-Newton matrices with lim- ited storage

    Jorge Nocedal. Updating Quasi-Newton matrices with lim- ited storage. Mathematics of Computation , 35:773–782,

  15. [23]

    Numerical Optimization

    Jorge Nocedal and Stephen Wright. Numerical Optimization. Springer, 2006. 3

    2006

  16. [24]

    Paschalis Panteleris, Iasonas Oikonomidis, and Antonis A. Argyros. Using a single RGB frame for real time 3D hand pose estimation in the wild. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) , pages 436– 445, 2017. 2

    2018

  17. [25]

    Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. Expressive body capture: 3D hands, face, and body from a single image. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pag...

    2019

  18. [26]

    Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bod- ies together. ACM Transactions on Graphics, (Proc. SIG- GRAPH Asia), 36(6), Nov. 2017. 1, 2, 4

    2017

  19. [27]

    Matthews, and Yaser Sheikh

    Tomas Simon, Hanbyul Joo, I. Matthews, and Yaser Sheikh. Hand keypoint detection in single images using multiview bootstrapping. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4645–4653, 2017. 2

    2017

  20. [28]

    Cross-modal deep variational hand pose estimation

    Adrian Spurr, Jie Song, Seonwook Park, and Otmar Hilliges. Cross-modal deep variational hand pose estimation. In 2018 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 89–98, 2018. 7

    2018

  21. [29]

    Real-time joint tracking of a hand manipulating an object from RGB-D input

    Srinath Sridhar, Franziska Mueller, Michael Zollhoefer, Dan Casas, Antti Oulasvirta, and Christian Theobalt. Real-time joint tracking of a hand manipulating an object from RGB-D input. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 6

    2016

  22. [30]

    Capturing hands in action using discriminative salient points and physics simulation

    Dimitrios Tzionas, Luca Ballan, Abhilash Srikantha, Pablo Aponte, Marc Pollefeys, and Juergen Gall. Capturing hands in action using discriminative salient points and physics simulation. International Journal of Computer Vision , 118(2):172–193, Mar. 2016. 3

    2016

  23. [31]

    Monocu- lar total capture: Posing face, body, and hands in the wild

    Donglai Xiang, Hanbyul Joo, and Yaser Sheikh. Monocu- lar total capture: Posing face, body, and hands in the wild. 2019 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 10957–10966, 2018. 7

    2019

  24. [32]

    End-to-end hand mesh recovery from a monocular RGB im- age

    Xiong Zhang, Qiang Li, Wenbo Zhang, and Wen Zheng. End-to-end hand mesh recovery from a monocular RGB im- age. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2354–2364, 2019. 2, 7

    2019

  25. [33]

    Monocular real- time hand shape and motion capture using multi-modal data

    Yuxiao Zhou, Marc Habermann, Weipeng Xu, Ikhsanul Habibie, Christian Theobalt, and Feng Xu. Monocular real- time hand shape and motion capture using multi-modal data. In Proceedings of the IEEE International Conference on Computer Vision, 2020. 5, 7

    2020

  26. [34]

    Learning to es- timate 3D hand pose from single RGB images

    Christiane Zimmermann and Thomas Brox. Learning to es- timate 3D hand pose from single RGB images. 2017 IEEE International Conference on Computer Vision (ICCV), pages 4913–4921, 2017. 2, 7

    2017

  27. [35]

    Russell, Max Argus, and Thomas Brox

    Christiane Zimmermann, Duygu Ceylan, Jimei Yang, Bryan C. Russell, Max Argus, and Thomas Brox. Frei- HAND: A dataset for markerless capture of hand pose and shape from single RGB images. 2019 IEEE/CVF Interna- tional Conference on Computer Vision (ICCV) , pages 813– 822, 2019. 2 9

    2019

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