The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
PARE: Part Attention Regressor for 3D Human Body Estimation
Open accessAbstract
Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable. To address this, we introduce a soft attention mechanism, called the Part Attention REgressor (PARE), that learns to predict body-part-guided attention masks. We observe that state-of-the-art methods rely on global feature representations, making them sensitive to even small occlusions. In contrast, PARE’s part-guided attention mechanism overcomes these issues by exploiting information about the visibility of individual body parts while leveraging information from neighboring body-parts to predict occluded parts. We show qualitatively that PARE learns sensible attention masks, and quantitative evaluation confirms that PARE achieves more accurate and robust reconstruction results than existing approaches on both occlusion-specific and standard benchmarks. The code and data are available for research purposes at https://pare.is.tue.mpg.de/
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Microsoft COCO: Common Objects in Context
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Visualizing and understanding convolutional networks
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Identity mappings in deep residual networks
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Identity Mappings in Deep Residual Networks
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Deep High-Resolution Representation Learning for Human Pose Estimation
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Self-citationHuman3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
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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
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
Li, Z., Liu, J., Zhang, Z., Xu, S., & Yan, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20065-6_34 · Cited by 268
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
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Metadata
- Authors
- Kocabas, M., Huang, C.-H. P., Hilliges, O., & Black, M. J.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 11107-11117
- OpenAlex
- W3156666908
- Cited by
- 417
- References
- 57
- FWCI
- 26.25
- Type
- conference paper
- Language
- en
- Open access
- green
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