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Showing content from https://github.com/open-mmlab/mmpose/releases/tag/v1.3.0 below:

Release MMPose v1.3.0 Release Note · open-mmlab/mmpose · GitHub

RTMO

We are exited to release RTMO:

RTMO is the first one-stage pose estimation method that achieves both high accuracy and real-time speed.
It performs best on crowded scenes. RTMO achieves 83.8% AP on the CrowdPose test set.
RTMO is easy to run for inference and deployment. It does not require an extra human detector.
Try it online with this demo by choosing rtmo | body.
The paper is available on arXiv.

Improved RTMW

We have released additional RTMW models in various sizes:

Config Input Size Whole AP Whole AR FLOPS^(G) RTMW-m 256x192 58.2 67.3 4.3 RTMW-l 256x192 66.0 74.6 7.9 RTMW-x 256x192 67.2 75.2 13.1 RTMW-l 384x288 70.1 78.0 17.7 RTMW-x 384x288 70.2 78.1 29.3

The hand keypoint detection accuracy has been notably improved.

Pose Anything

We are glad to support the inference for the category-agnostic pose estimation method PoseAnything!

You can now specify ANY keypoints you want the model to detect, without needing extra training. Under the project folder:

Download the pretrained model

Run:

python demo.py --support [path_to_support_image] --query [path_to_query_image] --config configs/demo_b.py --checkpoint [path_to_pretrained_ckpt]

Thanks to the author of PoseAnything (@orhir) for supporting their excellent work!

New Datasets

We have added support for two new datasets:

(CVPR 2023) ExLPose
(ICCV 2023) H3WB

(CVPR 2023) ExLPose

ExLPose builds a new dataset of real low-light images with accurate pose labels. It can be helpful on tranining a pose estimation model working under extreme light conditions.

Thanks @Yang-Changhui for helping with the integration of ExLPose!
This is the task of our OpenMMLabCamp, if you also wish to contribute code to us, feel free to refer to this link to pick up the task!

(ICCV 2023) H3WB

H3WB (Human3.6M 3D WholeBody) extends the Human3.6M dataset with 3D whole-body annotations using the COCO wholebody skeleton. This dataset enables more comprehensive 3D pose analysis and benchmarking for whole-body methods.

Supported by @xiexinch.

Contributors

@Tau-J
@Ben-Louis
@xiexinch
@Yang-Changhui
@orhir
@RFYoung
@yao5401
@icynic
@Jendker
@willyfh
@jit-a3
@Ginray

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