DEKR is a pose estimation model pretrained on COCO 2017 and THE Crowd Pose dataset.

Zigang Geng, Ke Sun, Bin Xiao , Zhaoxiang Zhang , Jingdong Wang, in the paper, “Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

Submitted Version
April 6, 2021

Latest Version


Pose Estimation


Model Highlights

  • Task: Pose Estimation
  • Model type: Convolutional Neural Network (CNN)
  • Framework: PyTorch
  • Dataset: COCO 2017 Keypoint Detection Dataset

Model Size and Parameters

In disentangled keypoint regression (DEKR), each branch learns the representation for one keypoint through two adaptive convolutions from a partition of feature maps output from the backbone and regresses the 2D offset of each keypoint using a 1×1 convolution separately. An illustration for three key points is shown in the figure below, the feature maps are divided into three partitions, each fed into one branch. In the experiments on COCO pose estimation, the feature maps are divided into 17 partitions and there are 17 branches for regressing the 17 key points.

DEKR adopts the multi-branch parallel adaptive convolutions to learn disentangled representations for the regression of the key points, so that each representation focuses on the corresponding keypoint region.

Expected Input

The DEKR model takes an image as input and predicts the human poses for all the persons in the image, where each pose consists of K keypoints, such as shoulder, elbow, and so on. 

Expected Output

DEKR outputs an image with a regressed pose at each position, and the keypoint and center heatmaps.

History and Applications

Human Pose Estimation identifies and classifies the poses of human body parts and joints in images or videos. The human pose estimation can be classified into two primary approaches: bottom-up and top-down. Bottom-up methods evaluate each body joint first and then arrange them to compose a unique pose. Top-down methods run a body detector first and determine body joints within the discovered bounding boxes. Different libraries are available on the internet for human pose estimation, which includes OpenPose, DensePose, AlphaPose, and HRNet

Some real-world applications of pose estimation include:

  • AI-powered personal trainers
  • Robotics
  • Motion capture and augmented reality
  • Athlete pose detection
  • Motion tracking for gaming
  • Infant Motion Analysis
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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")