MMPose v1.3.0 Release Note
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
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
