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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) |
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