Learning-Based Post-Processing methods

We provide two learning-based post-processing methods deciwatch and smoothnet for speed-up and smoothing respectively.

DeciWatch

We provide the config files for DeciWatch: DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation.

@article{zeng2022deciwatch,
  title={DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation},
  author={Zeng, Ailing and Ju, Xuan and Yang, Lei and Gao, Ruiyuan and Zhu, Xizhou and Dai, Bo and Xu, Qiang},
  journal={arXiv preprint arXiv:2203.08713},
  year={2022}
}

Notes

We use checkpoints trained on SPIN-3DPW for demo speed up. Checkpoints with different intervals and q values are provided. If you need more checkpoints trained on various datasets and backbones, please refer to the official implementation of DeciWatch.

Interval	Window Q	Config	Download	Speed Up	Precision Improvement (MPJPE In/Out)
10	1	deciwatch_interval10_q1	model	10X	99.35 / 95.85
10	2	deciwatch_interval10_q2	model	10X	99.45 / 96.37
10	3	deciwatch_interval10_q3	model	10X	99.60 / 96.98
10	4	deciwatch_interval10_q4	model	10X	99.58 / 96.87
10	5	deciwatch_interval10_q5	model	10X	99.78 / 97.39
5	1	deciwatch_interval5_q1	model	5X	99.31 / 95.05
5	2	deciwatch_interval5_q2	model	5X	99.35 / 95.05
5	3	deciwatch_interval5_q3	model	5X	99.45 / 94.84
5	4	deciwatch_interval5_q4	model	5X	99.45 / 94.94
5	5	deciwatch_interval5_q5	model	5X	99.55 / 94.48

To use different settings of DeciWatch in demo, specify --speed_up_type with the checkpoint name. For example, you may use --speed_up_type deciwatch_interval10_q3 for 10X speed up with a window size of 31. Simply set --speed_up_type deciwatch to use default setting deciwatch_interval5_q3. The meaning of interval and q can be found in the original paper.

SmoothNet

We provide the config files for SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos.

@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}
}

Notes

We use checkpoints trained on SPIN-3DPW for demo pose smoothing. Checkpoints with different window size are provided.

Window Size	Config	Download	Precision Improvement (MPJPE In/Out)	Smoothness Improvement (Accel In/Out)
8	smoothnet_windowsize8	model	96.85 / 95.84	34.62 / 7.13
16	smoothnet_windowsize16	model	96.85 / 95.61	34.62 / 6.35
32	smoothnet_windowsize32	model	96.85 / 95.03	34.62 / 6.11
64	smoothnet_windowsize64	model	96.85 / 95.26	34.62 / 6.02

To use different settings of SmoothNet in demo, specify --smooth_type with the checkpoint name. For example, you may use --smooth_type smoothnet_windowsize8 for pose smoothing with a window size of 8. Simply set --mooth_type smoothnet to use default setting smoothnet_windowsize8. The meaning of windowsize can be found in the original paper.