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<!-- [OTHERS] --> | |
<details> | |
<summary align="right"><a href="https://arxiv.org/abs/2112.13715">SmoothNet (arXiv'2021)</a></summary> | |
```bibtex | |
@article{zeng2021smoothnet, | |
title={SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos}, | |
author={Zeng, Ailing and Yang, Lei and Ju, Xuan and Li, Jiefeng and Wang, Jianyi and Xu, Qiang}, | |
journal={arXiv preprint arXiv:2112.13715}, | |
year={2021} | |
} | |
``` | |
</details> | |
<!-- [DATASET] --> | |
<details> | |
<summary align="right"><a href="https://ieeexplore.ieee.org/abstract/document/6682899/">Human3.6M (TPAMI'2014)</a></summary> | |
```bibtex | |
@article{h36m_pami, | |
author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu, Cristian}, | |
title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments}, | |
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, | |
publisher = {IEEE Computer Society}, | |
volume = {36}, | |
number = {7}, | |
pages = {1325-1339}, | |
month = {jul}, | |
year = {2014} | |
} | |
``` | |
</details> | |
The following SmoothNet model checkpoints are available for pose smoothing. The table shows the the performance of [SimpleBaseline3D](https://arxiv.org/abs/1705.03098) on [Human3.6M](https://ieeexplore.ieee.org/abstract/document/6682899/) dataset without/with the SmoothNet plugin, and compares the SmoothNet models with 4 different window sizes (8, 16, 32 and 64). The metrics are MPJPE(mm), P-MEJPE(mm) and Acceleration Error (mm/frame^2). | |
| Arch | Window Size | MPJPE<sup>w/o</sup> | MPJPE<sup>w</sup> | P-MPJPE<sup>w/o</sup> | P-MPJPE<sup>w</sup> | AC. Err<sup>w/o</sup> | AC. Err<sup>w</sup> | ckpt | | |
| :----------------------------------- | :---------: | :-----------------: | :---------------: | :-------------------: | :-----------------: | :-------------------: | :-----------------: | :-----------------------------------: | | |
| [smoothnet_ws8](/configs/_base_/filters/smoothnet_t8_h36m.py) | 8 | 54.48 | 53.15 | 42.20 | 41.32 | 19.18 | 1.87 | [ckpt](https://download.openmmlab.com/mmpose/plugin/smoothnet/smoothnet_ws8_h36m.pth) | | |
| [smoothnet_ws16](/configs/_base_/filters/smoothnet_t16_h36m.py) | 16 | 54.48 | 52.74 | 42.20 | 41.20 | 19.18 | 1.22 | [ckpt](https://download.openmmlab.com/mmpose/plugin/smoothnet/smoothnet_ws16_h36m.pth) | | |
| [smoothnet_ws32](/configs/_base_/filters/smoothnet_t32_h36m.py) | 32 | 54.48 | 52.47 | 42.20 | 40.84 | 19.18 | 0.99 | [ckpt](https://download.openmmlab.com/mmpose/plugin/smoothnet/smoothnet_ws32_h36m.pth) | | |
| [smoothnet_ws64](/configs/_base_/filters/smoothnet_t64_h36m.py) | 64 | 54.48 | 53.37 | 42.20 | 40.77 | 19.18 | 0.92 | [ckpt](https://download.openmmlab.com/mmpose/plugin/smoothnet/smoothnet_ws64_h36m.pth) | | |