Dev 1.x

CITATION.cff

@@ -1,4 +1,4 @@
-cff-version: 1.2.0
+cff-version: 1.3.0
 message: "If you use this software, please cite it as below."
 authors:
   - name: "MMPose Contributors"

README.md

@@ -98,21 +98,18 @@ 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](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw_cocktail14.md) models in various sizes ranging from RTMW-m to RTMW-x. The input sizes include `256x192` and `384x288`. 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)
+- Support inference of [PoseAnything](/projects/pose_anything). Web demo is available [here](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything).
 
-- 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 for two new datasets:
 
-![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)
 
 - 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:
 
@@ -121,6 +118,8 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
   - Utilize the powerful capabilities of MMPose in the form of independent projects without being constrained by the code framework.
   - Newly added projects include:
     - [RTMPose](/projects/rtmpose/)
+    - [RTMO](/projects/rtmo/)
+    - [PoseAnything](/projects/pose_anything/)
     - [YOLOX-Pose](/projects/yolox_pose/)
     - [MMPose4AIGC](/projects/mmpose4aigc/)
     - [Simple Keypoints](/projects/skps/)
@@ -130,15 +129,14 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
 <br/>
 
-- October 12, 2023: MMPose [v1.2.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) has been officially released, with major updates including:
+- January 4, 2024: MMPose [v1.3.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) has been officially released, with major updates including:
 
-  - Support for new datasets: UBody, 300W-LP.
-  - Support for new algorithms: MotionBERT, DWPose, EDPose, Uniformer.
-  - Migration of Associate Embedding, InterNet, YOLOX-Pose algorithms.
-  - Migration of the DeepFashion2 dataset.
-  - Support for Badcase visualization analysis, multi-dataset evaluation, and keypoint visibility prediction features.
+  - Support for new datasets: ExLPose, H3WB
+  - Release of new RTMPose series models: RTMO, RTMW
+  - Support for new algorithm PoseAnything
+  - Enhanced Inferencer with optional progress bar and improved affinity for one-stage methods
 
-  Please check the complete [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) for more details on the updates brought by MMPose v1.2.0!
+  Please check the complete [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) for more details on the updates brought by MMPose v1.3.0!
 
 ## 0.x / 1.x Migration
 

README_CN.md

@@ -96,21 +96,18 @@ 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
 
-  - (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)
 
-- 支持四个新算法：
+- 发布了不同尺寸的 [RTMW](/configs/wholebody_2d_keypoint/rtmpose/cocktail14/rtmw_cocktail14.md) 模型，满足不同的使用场景。模型尺寸覆盖从 RTMW-m 到 RTMW-x 的模型，输入图像尺寸包含 256x192 和 384x288
 
-  - (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：
 
@@ -119,6 +116,8 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
   - 通过独立项目的形式，利用 MMPose 的强大功能，同时不被代码框架所束缚
   - 最新添加的项目包括：
     - [RTMPose](/projects/rtmpose/)
+    - [RTMO](/projects/rtmo/)
+    - [PoseAnything](/projects/pose_anything/)
     - [YOLOX-Pose](/projects/yolox_pose/)
     - [MMPose4AIGC](/projects/mmpose4aigc/)
     - [Simple Keypoints](/projects/skps/)
@@ -128,15 +127,14 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-
 
 <br/>
 
-- 2023-10-12：MMPose [v1.2.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) 正式发布了，主要更新包括:
+- 2024-01-04：MMPose [v1.3.0](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) 正式发布了，主要更新包括:
 
-  - 支持新数据集：UBody、300W-LP。
-  - 支持新算法：MotionBERT、DWPose、EDPose、Uniformer
-  - 迁移 Associate Embedding、InterNet、YOLOX-Pose 算法。
-  - 迁移 DeepFashion2 数据集。
-  - 支持 Badcase 可视化分析、多数据集评测、关键点可见性预测功能。
+  - 支持新数据集：ExLPose、H3WB
+  - 发布 RTMPose 系列新模型：RTMO、RTMW
+  - 支持新算法 PoseAnything
+  - 推理器 Inferencer 支持可选的进度条、提升与单阶段模型的适配性
 
-  请查看完整的 [版本说明](https://github.com/open-mmlab/mmpose/releases/tag/v1.2.0) 以了解更多 MMPose v1.2.0 带来的更新!
+  请查看完整的 [版本说明](https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0) 以了解更多 MMPose v1.3.0 带来的更新!
 
 ## 0.x / 1.x 迁移
 

configs/_base_/datasets/exlpose.py

@@ -0,0 +1,125 @@
+dataset_info = dict(
+    dataset_name='exlpose',
+    paper_info=dict(
+        author='Sohyun Lee, Jaesung Rim, Boseung Jeong, Geonu Kim,'
+        'ByungJu Woo, Haechan Lee, Sunghyun Cho, Suha Kwak',
+        title='Human Pose Estimation in Extremely Low-Light Conditions',
+        container='arXiv',
+        year='2023',
+        homepage='https://arxiv.org/abs/2303.15410',
+    ),
+    keypoint_info={
+        0:
+        dict(
+            name='left_shoulder',
+            id=0,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_shoulder'),
+        1:
+        dict(
+            name='right_shoulder',
+            id=1,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_shoulder'),
+        2:
+        dict(
+            name='left_elbow',
+            id=2,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_elbow'),
+        3:
+        dict(
+            name='right_elbow',
+            id=3,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_elbow'),
+        4:
+        dict(
+            name='left_wrist',
+            id=4,
+            color=[0, 255, 0],
+            type='upper',
+            swap='right_wrist'),
+        5:
+        dict(
+            name='right_wrist',
+            id=5,
+            color=[255, 128, 0],
+            type='upper',
+            swap='left_wrist'),
+        6:
+        dict(
+            name='left_hip',
+            id=6,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_hip'),
+        7:
+        dict(
+            name='right_hip',
+            id=7,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_hip'),
+        8:
+        dict(
+            name='left_knee',
+            id=8,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_knee'),
+        9:
+        dict(
+            name='right_knee',
+            id=9,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_knee'),
+        10:
+        dict(
+            name='left_ankle',
+            id=10,
+            color=[0, 255, 0],
+            type='lower',
+            swap='right_ankle'),
+        11:
+        dict(
+            name='right_ankle',
+            id=11,
+            color=[255, 128, 0],
+            type='lower',
+            swap='left_ankle'),
+        12:
+        dict(name='head', id=12, color=[51, 153, 255], type='upper', swap=''),
+        13:
+        dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='')
+    },
+    skeleton_info={
+        0: dict(link=('head', 'neck'), id=0, color=[51, 153, 255]),
+        1: dict(link=('neck', 'left_shoulder'), id=1, color=[51, 153, 255]),
+        2: dict(link=('neck', 'right_shoulder'), id=2, color=[51, 153, 255]),
+        3: dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]),
+        4: dict(link=('left_elbow', 'left_wrist'), id=4, color=[0, 255, 0]),
+        5: dict(
+            link=('right_shoulder', 'right_elbow'), id=5, color=[255, 128, 0]),
+        6:
+        dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]),
+        7: dict(link=('neck', 'right_hip'), id=7, color=[51, 153, 255]),
+        8: dict(link=('neck', 'left_hip'), id=8, color=[51, 153, 255]),
+        9: dict(link=('right_hip', 'right_knee'), id=9, color=[255, 128, 0]),
+        10:
+        dict(link=('right_knee', 'right_ankle'), id=10, color=[255, 128, 0]),
+        11: dict(link=('left_hip', 'left_knee'), id=11, color=[0, 255, 0]),
+        12: dict(link=('left_knee', 'left_ankle'), id=12, color=[0, 255, 0]),
+    },
+    joint_weights=[
+        0.2, 0.2, 0.2, 1.3, 1.5, 0.2, 1.3, 1.5, 0.2, 0.2, 0.5, 0.2, 0.2, 0.5
+    ],
+    sigmas=[
+        0.079, 0.079, 0.072, 0.072, 0.062, 0.062, 0.107, 0.107, 0.087, 0.087,
+        0.089, 0.089, 0.079, 0.079
+    ])