[Docs] update readme (#2902)

open-mmlab · Jan 3, 2024 · 575f65a · 575f65a
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diff --git a/README.md b/README.md
@@ -98,21 +98,26 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
 ## What's New
 
-- We have added support for two new datasets:
+- Release [RTMO](/projects/rtmo), a state-of-the-art real-time method for multi-person pose estimation.
 
-  - (CVPR 2023) [UBody](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ubody-cvpr-2023)
-  - [300W-LP](https://github.com/open-mmlab/mmpose/tree/main/configs/face_2d_keypoint/topdown_heatmap/300wlp)
+  ![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2)
 
-- Support for four new algorithms:
+- Release RTMW models in various sizes. This provides flexibility to select the right model for different speed and accuracy requirements.
 
-  - (ICCV 2023) [MotionBERT](https://github.com/open-mmlab/mmpose/tree/main/configs/body_3d_keypoint/motionbert)
-  - (ICCVW 2023) [DWPose](https://github.com/open-mmlab/mmpose/tree/main/configs/wholebody_2d_keypoint/dwpose)
-  - (ICLR 2023) [EDPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo/body_2d_keypoint.html#edpose-edpose-on-coco)
-  - (ICLR 2022) [Uniformer](https://github.com/open-mmlab/mmpose/tree/main/projects/uniformer)
+  | Arch                                                        | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR | Hand AP | Hand AR | Whole AP | Whole AR |                            ckpt                             |
+  | :---------------------------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :---------------------------------------------------------: |
+  | [rtmw-m](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-m_8xb1024-270e_cocktail14-256x192.py) |  256x192   |  0.676  |  0.747  |  0.671  |  0.794  |  0.783  |  0.854  |  0.491  |  0.604  |  0.582   |  0.673   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-l-m_simcc-cocktail14_270e-256x192-20231122.pth) |
+  | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb1024-270e_cocktail14-256x192.py) |  256x192   |  0.743  |  0.807  |  0.763  |  0.868  |  0.834  |  0.889  |  0.598  |  0.701  |  0.660   |  0.746   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-256x192-20231122.pth) |
+  | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb704-270e_cocktail14-256x192.py) |  256x192   |  0.746  |  0.808  |  0.770  |  0.869  |  0.844  |  0.896  |  0.610  |  0.710  |  0.672   |  0.752   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-256x192-13a2546d_20231208.pth) |
+  | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb320-270e_cocktail14-384x288.py) |  384x288   |  0.761  |  0.824  |  0.793  |  0.885  |  0.884  |  0.921  |  0.663  |  0.752  |  0.701   |  0.780   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-384x288-20231122.pth) |
+  | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb320-270e_cocktail14-384x288.py) |  384x288   |  0.763  |  0.826  |  0.796  |  0.888  |  0.884  |  0.923  |  0.664  |  0.755  |  0.702   |  0.781   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-384x288-f840f204_20231122.pth) |
 
-- Released the first whole-body pose estimation model, RTMW, with accuracy exceeding 70 AP on COCO-Wholebody. For details, refer to [RTMPose](/projects/rtmpose/). [Try it now!](https://openxlab.org.cn/apps/detail/mmpose/RTMPose)
+- Support inference of [PoseAnything](/projects/pose_anything). Web demo is available [here](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything).
 
-![rtmw](https://github.com/open-mmlab/mmpose/assets/13503330/635c4618-c459-45e8-84a5-eb68cf338d00)
+- Support for two new datasets:
+
+  - (CVPR 2023) [ExLPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#exlpose-dataset)
+  - (ICCV 2023) [H3WB](/docs/en/dataset_zoo/3d_wholebody_keypoint.md)
 
 - Welcome to use the [*MMPose project*](/projects/README.md). Here, you can discover the latest features and algorithms in MMPose and quickly share your ideas and code implementations with the community. Adding new features to MMPose has become smoother:
 

diff --git a/README_CN.md b/README_CN.md
@@ -96,21 +96,26 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
 ## 最新进展
 
-- 我们支持了两个新的数据集:
+- 发布了单阶段实时多人姿态估计模型 [RTMO](/projects/rtmo)。相比 RTMPose 在多人场景下性能更优
+
+  ![rtmo](https://github.com/open-mmlab/mmpose/assets/26127467/54d5555a-23e5-4308-89d1-f0c82a6734c2)
 
-  - (CVPR 2023) [UBody](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo_papers/datasets.html#ubody-cvpr-2023)
-  - [300W-LP](https://github.com/open-mmlab/mmpose/tree/main/configs/face_2d_keypoint/topdown_heatmap/300wlp)
+- 发布了不同尺寸的 RTMW 模型，满足不同的使用场景
 
-- 支持四个新算法：
+  | Arch                                                        | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR | Hand AP | Hand AR | Whole AP | Whole AR |                            ckpt                             |
+  | :---------------------------------------------------------- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :------: | :---------------------------------------------------------: |
+  | [rtmw-m](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-m_8xb1024-270e_cocktail14-256x192.py) |  256x192   |  0.676  |  0.747  |  0.671  |  0.794  |  0.783  |  0.854  |  0.491  |  0.604  |  0.582   |  0.673   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-l-m_simcc-cocktail14_270e-256x192-20231122.pth) |
+  | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb1024-270e_cocktail14-256x192.py) |  256x192   |  0.743  |  0.807  |  0.763  |  0.868  |  0.834  |  0.889  |  0.598  |  0.701  |  0.660   |  0.746   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-256x192-20231122.pth) |
+  | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb704-270e_cocktail14-256x192.py) |  256x192   |  0.746  |  0.808  |  0.770  |  0.869  |  0.844  |  0.896  |  0.610  |  0.710  |  0.672   |  0.752   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-256x192-13a2546d_20231208.pth) |
+  | [rtmw-l](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-l_8xb320-270e_cocktail14-384x288.py) |  384x288   |  0.761  |  0.824  |  0.793  |  0.885  |  0.884  |  0.921  |  0.663  |  0.752  |  0.701   |  0.780   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-dw-x-l_simcc-cocktail14_270e-384x288-20231122.pth) |
+  | [rtmw-x](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw-x_8xb320-270e_cocktail14-384x288.py) |  384x288   |  0.763  |  0.826  |  0.796  |  0.888  |  0.884  |  0.923  |  0.664  |  0.755  |  0.702   |  0.781   | [ckpt](https://download.openmmlab.com/mmpose/v1/projects/rtmw/rtmw-x_simcc-cocktail14_pt-ucoco_270e-384x288-f840f204_20231122.pth) |
 
-  - (ICCV 2023) [MotionBERT](https://github.com/open-mmlab/mmpose/tree/main/configs/body_3d_keypoint/motionbert)
-  - (ICCVW 2023) [DWPose](https://github.com/open-mmlab/mmpose/tree/main/configs/wholebody_2d_keypoint/dwpose)
-  - (ICLR 2023) [EDPose](https://mmpose.readthedocs.io/zh_CN/latest/model_zoo/body_2d_keypoint.html#edpose-edpose-on-coco)
-  - (ICLR 2022) [Uniformer](https://github.com/open-mmlab/mmpose/tree/main/projects/uniformer)
+- 支持了 [PoseAnything](/projects/pose_anything) 的推理。[在线试玩](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything)
 
-- 发布首个在 COCO-Wholebody 上精度超过 70 AP 的全身姿态估计模型 RTMW，具体请参考 [RTMPose](/projects/rtmpose/)。[在线试玩](https://openxlab.org.cn/apps/detail/mmpose/RTMPose)
+- 我们支持了两个新的数据集:
 
-![rtmw](https://github.com/open-mmlab/mmpose/assets/13503330/635c4618-c459-45e8-84a5-eb68cf338d00)
+  - (CVPR 2023) [ExLPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#exlpose-dataset)
+  - (ICCV 2023) [H3WB](/docs/en/dataset_zoo/3d_wholebody_keypoint.md)
 
 - 欢迎使用 [*MMPose 项目*](/projects/README.md)。在这里，您可以发现 MMPose 中的最新功能和算法，并且可以通过最快的方式与社区分享自己的创意和代码实现。向 MMPose 中添加新功能从此变得简单丝滑：
 

diff --git a/docs/en/index.rst b/docs/en/index.rst
@@ -81,6 +81,7 @@ You can change the documentation language at the lower-left corner of the page.
    dataset_zoo/2d_animal_keypoint.md
    dataset_zoo/3d_body_keypoint.md
    dataset_zoo/3d_hand_keypoint.md
+   dataset_zoo/3d_wholebody_keypoint.md
 
 .. toctree::
    :maxdepth: 1