The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Open accessAbstract
Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images. By directly mapping raw pixels to model parameters, these methods can produce parametric models in a feed-forward manner via neural networks. However, minor deviation in parameters may lead to noticeable mis-alignment between the estimated meshes and image evidences. To address this issue, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status in our deep regressor. In PyMAF, given the currently predicted parameters, mesh-aligned evidences will be extracted from finer-resolution features accordingly and fed back for parameter rectification. To reduce noise and enhance the reliability of these evidences, an auxiliary pixel-wise supervision is imposed on the feature encoder, which provides mesh-image correspondence guidance for our network to preserve the most related information in spatial features. The efficacy of our approach is validated on several benchmarks, including Human3.6M, 3DPW, LSP, and COCO, where experimental results show that our approach consistently improves the mesh-image alignment of the reconstruction. The project page with code and video results can be found at https://hongwenzhang.github.io/pymaf.
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Cites
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Feature Pyramid Networks for Object Detection
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Deep High-Resolution Representation Learning for Human Pose Estimation
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“, SCAPE [3] and SMPL [34]) to 2D observations such as keypoints and silhouettes [48, 13, 5].”
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MotionBERT: A Unified Perspective on Learning Human Motion Representations
Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., & Wang, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01385 · Cited by 348
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion
Black, M. J., Patel, P., Tesch, J., & Yang, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00843 · Cited by 148
Metadata
- Authors
- Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., & Sun, Z.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 11426-11436
- OpenAlex
- W3204956438
- Cited by
- 341
- References
- 68
- FWCI
- 20.62
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., & Sun, Z. (2021). PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.01125
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