fix typo and format md files (open-mmlab#1463)

ajgrafton · Jul 4, 2022 · 503c027 · 503c027
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diff --git a/...dy/3d_kpt_sview_rgb_img/pose_lift/mpi_inf_3dhp/simplebaseline3d_mpi-inf-3dhp.md b/...dy/3d_kpt_sview_rgb_img/pose_lift/mpi_inf_3dhp/simplebaseline3d_mpi-inf-3dhp.md
@@ -37,6 +37,6 @@ Results on MPI-INF-3DHP dataset with ground truth 2D detections
 
 | Arch                                                        | MPJPE | P-MPJPE | 3DPCK | 3DAUC |                             ckpt                             |                             log                             |
 | :---------------------------------------------------------- | :---: | :-----: | :---: | :---: | :----------------------------------------------------------: | :---------------------------------------------------------: |
-| [simple_baseline_3d_tcn<sup>1</sup>](configs/body/3d_kpt_sview_rgb_img/pose_lift/mpi_inf_3dhp/simplebaseline3d_mpi-inf-3dhp.py) | 84.3  |  53.2   | 85.0  | 52.0  | [ckpt](https://download.openmmlab.com/mmpose/body3d/simple_baseline/simplebaseline3d_mpi-inf-3dhp-b75546f6_20210603.pth) | [log](https://download.openmmlab.com/mmpose/body3d/simplebaseline3d/simplebaseline3d_mpi-inf-3dhp_20210603.log.json) |
+| [simple_baseline_3d_tcn<sup>1</sup>](/configs/body/3d_kpt_sview_rgb_img/pose_lift/mpi_inf_3dhp/simplebaseline3d_mpi-inf-3dhp.py) | 84.3  |  53.2   | 85.0  | 52.0  | [ckpt](https://download.openmmlab.com/mmpose/body3d/simple_baseline/simplebaseline3d_mpi-inf-3dhp-b75546f6_20210603.pth) | [log](https://download.openmmlab.com/mmpose/body3d/simplebaseline3d/simplebaseline3d_mpi-inf-3dhp_20210603.log.json) |
 
 <sup>1</sup> Differing from the original paper, we didn't apply the `max-norm constraint` because we found this led to a better convergence and performance.
diff --git a/docs/en/tasks/3d_body_mesh.md b/docs/en/tasks/3d_body_mesh.md
@@ -52,6 +52,11 @@ mmpose
 
 ### SMPL Model
 
+<!-- [DATASET] -->
+
+<details>
+<summary align="right"><a href="https://dl.acm.org/doi/abs/10.1145/2816795.2818013">SMPL (TOG'2015)</a></summary>
+
 ```bibtex
 @article{loper2015smpl,
   title={SMPL: A skinned multi-person linear model},
@@ -65,6 +70,8 @@ mmpose
 }
 ```
 
+</details>
+
 For human mesh estimation, SMPL model is used to generate the human mesh.
 Please download the [gender neutral SMPL model](http://smplify.is.tue.mpg.de/),
 [joints regressor](https://download.openmmlab.com/mmpose/datasets/joints_regressor_cmr.npy)
@@ -180,18 +187,23 @@ extract the images by themselves.
 
 <!-- [DATASET] -->
 
+<details>
+<summary align="right"><a href="https://ieeexplore.ieee.org/abstract/document/8374605/">MPI-INF-3DHP (3DV'2017)</a></summary>
+
 ```bibtex
 @inproceedings{mono-3dhp2017,
- author = {Mehta, Dushyant and Rhodin, Helge and Casas, Dan and Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and Theobalt, Christian},
- title = {Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision},
- booktitle = {3D Vision (3DV), 2017 Fifth International Conference on},
- url = {http://gvv.mpi-inf.mpg.de/3dhp_dataset},
- year = {2017},
- organization={IEEE},
- doi={10.1109/3dv.2017.00064},
+  author = {Mehta, Dushyant and Rhodin, Helge and Casas, Dan and Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and Theobalt, Christian},
+  title = {Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision},
+  booktitle = {3D Vision (3DV), 2017 Fifth International Conference on},
+  url = {http://gvv.mpi-inf.mpg.de/3dhp_dataset},
+  year = {2017},
+  organization={IEEE},
+  doi={10.1109/3dv.2017.00064},
 }
 ```
 
+</details>
+
 For [MPI-INF-3DHP](http://gvv.mpi-inf.mpg.de/3dhp-dataset/), please follow the
 [preprocess procedure](https://github.com/nkolot/SPIN/tree/master/datasets/preprocess)
 of SPIN to sample images, and make them like this:
@@ -241,6 +253,9 @@ mmpose
 
 <!-- [DATASET] -->
 
+<details>
+<summary align="right"><a href="http://www.bmva.org/bmvc/2010/conference/paper12/paper12.pdf">LSP (BMVC'2010)</a></summary>
+
 ```bibtex
 @inproceedings{johnson2010clustered,
   title={Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation.},
@@ -254,6 +269,8 @@ mmpose
 }
 ```
 
+</details>
+
 For [LSP](https://sam.johnson.io/research/lsp.html), please download the high resolution version
 [LSP dataset original](http://sam.johnson.io/research/lsp_dataset_original.zip).
 Extract them under `$MMPOSE/data`, and make them look like this:
@@ -277,6 +294,9 @@ mmpose
 
 <!-- [DATASET] -->
 
+<details>
+<summary align="right"><a href="https://ieeexplore.ieee.org/abstract/document/5995318/">LSPET (CVPR'2011)</a></summary>
+
 ```bibtex
 @inproceedings{johnson2011learning,
   title={Learning effective human pose estimation from inaccurate annotation},
@@ -288,6 +308,8 @@ mmpose
 }
 ```
 
+</details>
+
 For [LSPET](https://sam.johnson.io/research/lspet.html), please download its high resolution form
 [HR-LSPET](http://datasets.d2.mpi-inf.mpg.de/hr-lspet/hr-lspet.zip).
 Extract them under `$MMPOSE/data`, and make them look like this:
@@ -313,6 +335,9 @@ mmpose
 
 <!-- [DATASET] -->
 
+<details>
+<summary align="right"><a href="https://openaccess.thecvf.com/content_cvpr_2018/papers/Kanazawa_End-to-End_Recovery_of_CVPR_2018_paper.pdf">CMU MoShed (CVPR'2018)</a></summary>
+
 ```bibtex
 @inproceedings{kanazawa2018end,
   title={End-to-end recovery of human shape and pose},
@@ -323,6 +348,8 @@ mmpose
 }
 ```
 
+</details>
+
 Real-world SMPL parameters are used for the adversarial training in human mesh estimation.
 The MoShed data provided in [HMR](https://github.com/akanazawa/hmr) is included in this
 [zip file](https://download.openmmlab.com/mmpose/datasets/mesh_annotation_files.zip).