Standard
Motion Adaptive Pose Estimation from Compressed Videos. / Fan, Zhipeng
; Liu, Jun; Wang, Yao.
Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021. Institute of Electrical and Electronics Engineers Inc., 2022. p. 11699-11708 (Proceedings of the IEEE International Conference on Computer Vision).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Fan, Z
, Liu, J & Wang, Y 2022,
Motion Adaptive Pose Estimation from Compressed Videos. in
Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021. Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., pp. 11699-11708, 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021, Virtual, Online, Canada,
11/10/21.
https://doi.org/10.1109/ICCV48922.2021.01151APA
Vancouver
Fan Z
, Liu J, Wang Y.
Motion Adaptive Pose Estimation from Compressed Videos. In Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021. Institute of Electrical and Electronics Engineers Inc. 2022. p. 11699-11708. (Proceedings of the IEEE International Conference on Computer Vision). Epub 2021 Oct 10. doi: 10.1109/ICCV48922.2021.01151
Author
Fan, Zhipeng
; Liu, Jun ; Wang, Yao. /
Motion Adaptive Pose Estimation from Compressed Videos. Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 11699-11708 (Proceedings of the IEEE International Conference on Computer Vision).
Bibtex
@inproceedings{b27a8d615f2b4a9492c323aa43fa7618,
title = "Motion Adaptive Pose Estimation from Compressed Videos",
abstract = "Human pose estimation from videos has many real-world applications. Existing methods focus on applying models with a uniform computation profile on fully decoded frames, ignoring the freely-available motion signals and motion-compensation residuals from the compressed stream. A novel model, called Motion Adaptive Pose Net is proposed to exploit the compressed streams to efficiently decode pose sequences from videos. The model incorporates a Motion Compensated ConvLSTM to propagate the spatially aligned features, along with an adaptive gate to dynamically determine if the computationally expensive features should be extracted from fully decoded frames to compensate the motion-warped features, solely based on the residual errors. Leveraging the informative yet readily available signals from compressed streams, we propagate the latent features through our Motion Adaptive Pose Net efficiently Our model outperforms the state-of-the-art models in pose-estimation accuracy on two widely used datasets with only around half of the computation complexity.",
author = "Zhipeng Fan and Jun Liu and Yao Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 ; Conference date: 11-10-2021 Through 17-10-2021",
year = "2022",
month = feb,
day = "28",
doi = "10.1109/ICCV48922.2021.01151",
language = "English",
isbn = "9781665428132",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "11699--11708",
booktitle = "Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021",
}
RIS
TY - GEN
T1 - Motion Adaptive Pose Estimation from Compressed Videos
AU - Fan, Zhipeng
AU - Liu, Jun
AU - Wang, Yao
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2022/2/28
Y1 - 2022/2/28
N2 - Human pose estimation from videos has many real-world applications. Existing methods focus on applying models with a uniform computation profile on fully decoded frames, ignoring the freely-available motion signals and motion-compensation residuals from the compressed stream. A novel model, called Motion Adaptive Pose Net is proposed to exploit the compressed streams to efficiently decode pose sequences from videos. The model incorporates a Motion Compensated ConvLSTM to propagate the spatially aligned features, along with an adaptive gate to dynamically determine if the computationally expensive features should be extracted from fully decoded frames to compensate the motion-warped features, solely based on the residual errors. Leveraging the informative yet readily available signals from compressed streams, we propagate the latent features through our Motion Adaptive Pose Net efficiently Our model outperforms the state-of-the-art models in pose-estimation accuracy on two widely used datasets with only around half of the computation complexity.
AB - Human pose estimation from videos has many real-world applications. Existing methods focus on applying models with a uniform computation profile on fully decoded frames, ignoring the freely-available motion signals and motion-compensation residuals from the compressed stream. A novel model, called Motion Adaptive Pose Net is proposed to exploit the compressed streams to efficiently decode pose sequences from videos. The model incorporates a Motion Compensated ConvLSTM to propagate the spatially aligned features, along with an adaptive gate to dynamically determine if the computationally expensive features should be extracted from fully decoded frames to compensate the motion-warped features, solely based on the residual errors. Leveraging the informative yet readily available signals from compressed streams, we propagate the latent features through our Motion Adaptive Pose Net efficiently Our model outperforms the state-of-the-art models in pose-estimation accuracy on two widely used datasets with only around half of the computation complexity.
U2 - 10.1109/ICCV48922.2021.01151
DO - 10.1109/ICCV48922.2021.01151
M3 - Conference contribution/Paper
AN - SCOPUS:85126963367
SN - 9781665428132
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 11699
EP - 11708
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Y2 - 11 October 2021 through 17 October 2021
ER -
