Human pose estimation has become a cornerstone task in modern computer vision systems, powering applications from activity recognition to augmented reality. At the core of many state-of-the-art pose estimation models, including approaches like HigherHRNet, is the use of human pose estimation heatmaps.

These heatmaps are not just intermediate outputs, they are critical for understanding how pose estimation in computer vision systems localize and reason about the human body. By visualizing them, practitioners can inspect model confidence, debug errors, and gain insight into how predictions are formed.

In this post, we’ll go beyond theory and show how to generate, manipulate, and visualize heatmaps from a real-world human pose estimation pipeline, and how tools like FiftyOne can make this process significantly more interpretable and actionable.

A heatmap in computer vision is a visual representation often used to highlight areas of interest or intensity within an image. It typically assigns color gradients to different regions of the image based on the intensity or significance of certain features, such as object boundaries, key points, or activation values from neural networks.

Darker regions usually indicate lower intensity or lesser importance, while brighter or more vibrant areas signify higher intensity or greater significance in the context of the task at hand, aiding in understanding and analyzing the image's content and structure, especially for tasks like object detection, pose estimation, or saliency mapping.

In FiftyOne, heatmaps can be created in one of two ways. You can either point to the location of a saved map, or create a map in memory and load that instead. Checkout below to see how both of these are implemented:

Note that in the example above, the heatmap is a 2 dimensional array, where each value is the intensity of the heatmap at that pixel location. You can specify the range of your intensity values as well by passing the range argument.

With the code above, pixels with the value +9 will be rendered with 90% opacity, and pixels with the value -3 will be rendered with 30% opacity. Now moving on to pose estimation.

One of the most popular ways to use heatmaps today is with pose estimation. Heatmaps are a great localization tool to use when trying to create keypoint skeletons within your images. One such pose estimation model uses a heatmap to skeletons approach is the SWAHR-HumanPose model from CVPR 2021. A pose estimation model deserving of a post of its own, SWAHR is able to produce high quality results in astonishing inference times. Up next, we’ll take a look at the heatmaps it generates and how we can visualize them in FiftyOne.

If you’ve ever wondered what pose estimation is in practical terms, it’s the process of predicting structured keypoints (e.g., joints, limbs) that define the geometry of a human body. In contemporary pose estimation AI, this is typically achieved by training deep neural networks to output heatmaps, where each channel corresponds to a specific anatomical landmark.

The repo for the human pose estimation model unfortunately has not stood the test of time as well as the model results and can be a tricky set up to get running. If you are interested in getting it started, I included additional steps in the notebook. It will include instructions on rolling back some libraries like numpy and torch as well as building some C++ libraries. To get it all to work. You will also need to download one of the pretrained models linked here. I used pose_higher_hrnet_w32_512.pth in my example. Make sure the model is placed in the local ./models directory inside the repo.

We will be using the provided script dist_inference.py with a few modifications to save our heatmaps to disk. The script will take several command line options, most importantly the model_path, image_dir, and save_dir. On execution, the model will generate 18 different heatmaps, each for the keypoint of interest in the skeleton. We will then combine all these heatmaps to make one master heatmap and save it to disk. Let’s take a look at the main steps in these code snippets,: Setting up inference loop:

After setting up a loop for inference we can run the pose estimation model and parse the results.

Running our basic inference loop leaves us with our final_heatmaps object. Normally, this final_heatmaps array would be passed on to create the keypoint skeleton. However, in our case, we are interested in visualizing just our heatmaps. Therefore, we use the provided function make_heatmaps to grab our heatmaps in a form that is easier to understand. We then combine all of our heatmaps by taking the pointwise maximum.

Finally, with our master heatmap ready, our last step is to resize back to the original image size and save to disk.

With our code laid out, we will generate some heatmaps for our quickstart dataset. To execute the same as our example, run the following line to execute the model:

Congrats! With our new heatmaps saved to our disk, we can now load them back into FiftyOne. Take a moment to flip your virtual environment back to an environment on the latest FiftyOne version and let's try to load in our new heatmaps.

To start, we load in the quick start dataset that we used to inference on.

After our dataset is loaded in, we can compare the filepath of our dataset to the saved filepath of our mask to load in our new heatmaps:

After visualizing our results, we can see that our heatmap has been added. It is cool to note that the pose estimation model for generating the heatmaps has a very low false positive rate, and we do not see any heatmaps on non person samples.

By observing a person sample closer, we can begin to see the localization by the heatmap of each keypoint in our image. In the sample below, we can see various parts highlighted such as nose, eyes, ears, arms and legs.

The model even performs well from unusual angles and obstructed views.

Visualizing human pose estimation heatmaps transforms abstract model outputs into interpretable signals, making it easier to diagnose errors, validate predictions, and improve model performance. Rather than treating heatmaps as transient artifacts in a pipeline, elevating them to first-class visualizations enables deeper insight into how pose estimation models reason about spatial relationships and keypoint localization.

Within FiftyOne, this process becomes scalable and interactive. By aligning heatmaps with source imagery, teams can systematically analyze model behavior across datasets, identify edge cases, and accelerate iteration cycles in pose estimation computer vision workflows. This is especially valuable when working with complex, multi-scale inference pipelines like HigherHRNet, where understanding intermediate outputs is critical for optimization.

As pose estimation AI systems continue to evolve, the ability to inspect and interpret their outputs will be just as important as improving their accuracy.

If you’re building or deploying human pose estimation systems and want better visibility into your models, you can get started with Open Source FiftyOne or Book an enterprise demo to see how FiftyOne helps you visualize, debug, and optimize pose estimation at scale.