Amazon SageMaker Ground Truth provides a custom labeling workflow for keypoint and pose annotation using a skeleton-based user interface, enabling efficient and accurate labeling of large datasets for training pose estimation models.

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<p>This article discusses a custom labeling workflow for keypoint and pose annotation using Amazon SageMaker Ground Truth. It shows how to set up and deploy the necessary components for an online web portal where a labeling workforce can annotate images using a custom UI designed for skeleton-based pose labeling.</p>
<p>Specifically, the article covers:</p>
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<li>The importance of high-quality data and reducing labeling errors for training accurate pose estimation models</li>
<li>An overview of the solution architecture involving SageMaker Ground Truth, Amazon CloudFront, Amazon S3, AWS Lambda functions, and IAM roles</li>
<li>Prerequisites and steps for creating the AWS CDK stack to deploy the solution</li>
<li>Creating a sample labeling job and accessing the annotation results</li>
<li>Customizing the labeling UI for different keypoints, skeleton structures, and colors</li>
<li>Considerations for productionizing the workflow</li>
</ul>
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