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Cited by 576

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Efficient Human Pose Estimation from Single Depth Images

Shotton, Jamie, Girshick, Ross, Fitzgibbon, Andrew, Sharp, Toby, Cook, Mat, Finocchio, Mark, Moore, Richard, Kohli, Pushmeet, Criminisi, Antonio, Kipman, Alex, Blake, Andrew·2013·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2012.241
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Abstract

We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, and highly varied synthetic set of training images. This allows us to learn models that are largely invariant to factors such as pose, body shape, field-of-view cropping, and clothing. Our first approach employs an intermediate body parts representation, designed so that an accurate per-pixel classification of the parts will localize the joints of the body. The second approach instead directly regresses the positions of body joints. By using simple depth pixel comparison features and parallelizable decision forests, both approaches can run super-real time on consumer hardware. Our evaluation investigates many aspects of our methods, and compares the approaches to each other and to the state of the art. Results on silhouettes suggest broader applicability to other imaging modalities.

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  1. 10.1007/11550518_36 · DOI
  2. 10.1109/ROBOT.2006.1641949 · DOI
  3. 10.1007/978-3-540-76386-4_38 · doi:10.1007/978-3-540-76386-4_38
  4. 10.1109/CVPRW.2010.5543618 · DOI
  5. 10.1109/ROBOT.2010.5509559 · DOI · Paper page
  6. 10.1109/CVPR.2010.5540141 · DOI · Paper page
  7. 10.1109/CVPR.1998.698581 · DOI · Paper page
  8. 10.1109/CVPR.2004.1315063 · DOI · Paper page
  9. 10.1145/1531326.1531369 · DOI · Paper page
  10. 10.1007/s11263-009-0274-5 · doi:10.1007/s11263-009-0274-5
  11. 10.1007/978-3-540-88693-8_44 · DOI · Paper page
  12. 10.1109/CVPR.2008.4587360 · DOI · Paper page
  13. 10.1109/CVPR.2011.5995316 · DOI · Paper page
  14. 10.1109/CVPR.2003.1211479 · DOI · Paper page
  15. 10.1109/CVPR.2006.305 · DOI · Paper page
  16. 10.1109/ICCV.2009.5459303 · DOI · Paper page
  17. 10.1109/ICCV.2011.6126270 · DOI · Paper page
  18. 10.1109/34.1000236 · DOI · Paper page
  19. 10.1109/CVPR.2009.5206740 · doi:10.1109/cvpr.2009.5206740
  20. 10.1016/j.cviu.2006.08.002 · DOI · Paper page
  21. 10.1016/j.cviu.2006.10.016 · DOI · Paper page
  22. 10.1023/B:VISI.0000042934.15159.49 · DOI · Paper page
  23. 10.1023/A:1011179004708 · DOI · Paper page
  24. 10.1109/CVPR.2003.1211504 · DOI · Paper page
  25. 10.1109/CVPR.2008.4587436 · DOI · Paper page
  26. 10.1109/CVPR.2005.133 · DOI · Paper page
  27. 10.1145/1778765.1778839 · DOI · Paper page
  28. 10.1109/CVPR.2004.1315258 · DOI · Paper page
  29. 10.1109/CVPR.2007.383341 · DOI
  30. 10.1109/ICCV.2007.4408976 · DOI
  31. 10.1007/3-540-47977-5_44 · DOI · Paper page
  32. 10.1109/ICCV.2003.1238424 · DOI · Paper page
  33. 10.1109/CVPR.2008.4587617 · DOI · Paper page
  34. 10.1007/s11263-007-0095-3 · DOI · Paper page
  35. 10.1145/1877868.1877873 · DOI
  36. 10.1007/978-3-540-88688-4_32 · DOI
  37. 10.1109/CVPR.2008.4587534 · DOI
  38. 10.1007/3-540-47969-4_52 · DOI · Paper page
  39. 10.1016/0304-3975(85)90224-5 · DOI · Paper page
  40. 2013, CMU Mocap Database
  41. Autodesk MotionBuilder
  42. 10.1109/CVPR.2005.288 · DOI · Paper page
  43. Shepherd, An Appraisal of a Decision Tree Approach to Image Classification
  44. 10.1023/A:1022643204877 · doi:10.1023/a:1022643204877 · Paper page
  45. 10.1162/neco.1997.9.7.1545 · DOI · Paper page
  46. 10.29172/7c2a6982-6d72-4cd8-bba6-2fccb06a7011 · DOI · Paper page
  47. 10.1561/0600000035 · DOI · Paper page
  48. Moosmann, Fast Discriminative Visual Codebooks Using Randomized Clustering Forests, 19, 985
  49. 10.1109/CVPR.2008.4587503 · DOI · Paper page
  50. 10.1007/11744023_1 · DOI · Paper page
  51. Criminisi, Regression Forests for Efficient Anatomy Detection and Localization in CT Studies, 10.1007/978-3-642-18421-5_11 · DOI · Paper page
  52. 10.1109/34.1000236 · DOI · Paper page
  53. Nowozin, Improved Information Gain Estimates for Decision Tree Induction
  54. 10.1007/978-3-642-22092-0_16 · DOI
  55. 10.1145/3147.3165 · DOI · Paper page
  56. Gavrila, Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images

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Metadata

Authors
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., & Blake, A.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-12
Volume
35
Issue
12
Pages
2821-2840
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2012.241
OpenAlex
W2036196300
Cited by
576
References
6
FWCI
43.93
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Efficient Human Pose Estimation from Single Depth Images. https://app.cochat.ai/papers/10.1109/tpami.2012.241
The paper
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., & Blake, A. (2013). Efficient Human Pose Estimation from Single Depth Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2012.241

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.