Skeletons Out Of The Closet

Getting Started With Skeletons Out Of The Closet

Skeletons Out Of The Closet is a data curation tool for training computer vision models, especially ones that need pose estimation data. It handles keypoint extraction, labeling, and dataset organization. The repo lives on GitHub and it's free to use if you're comfortable with command line stuff and Python. I spent about three weeks wrestling with it to get a custom training pipeline working. Here's what actually happens when you use it, not the README version.

Skeletons Out Of The Closet Setup And Basic Usage

First, clone the repo and install dependencies. You need Python 3.8 or newer. The installation isn't exactly painful but it's not instant either — expect maybe ten to fifteen minutes depending on whether your system plays nice with OpenCV and torch. Once installed, the workflow runs in stages. You feed it images or video frames, it extracts skeleton keypoints using a pose estimation model underneath, then exports the results into a format your training script can consume. The default export is usually JSON or CSV with keypoint coordinates per image. The command structure looks like this at a basic level:

python -m skeletons_out_the_closet.run --input path/to/images --output path/to/output --model yolo-pose That's the surface level. There are flags for confidence thresholds, batch sizes, preprocessing options, and different backbone choices. The documentation lists them but doesn't always explain what happens when you change them.

What Actually Happens Under The Hood

When you run the pipeline, the tool first runs whatever pose estimation backbone you selected. YOLOv8-Pose is common. It outputs bounding boxes for people and then keypoint coordinates relative to each bounding box. Those coordinates get normalized to a 0-1 scale based on image dimensions, then written out. The normalization step matters more than people realize. If your training script expects absolute pixel coordinates and the tool is giving you normalized values, your model will learn nothing useful. I caught this by plotting the exported keypoints on a few sample images and noticing they were all clustered in the top-left corner at tiny scales. There's also a filtering step where low-confidence detections get dropped. The default confidence threshold is 0.5. If you're working with low-quality footage or unusual angles, that threshold might be too aggressive. I had to drop mine to 0.3 for a dataset of construction site images where people were partially occluded or far away. Everything above 0.3 still looked reasonable on visual inspection.

Common Pitfalls And Things The Docs Don't Highlight

One thing nobody warns you about is how the tool handles multiple people in a single frame. It assigns a person index to each detection, but if you have overlapping bounding boxes or people very close together, the indexing can get inconsistent between frames. This breaks temporal continuity if you're working with video. The fix is to run the multi-person tracking module separately and pass those track IDs through, which adds another step and some configuration overhead. Another issue is the coordinate system. Different pose models use different keypoint orderings. COCO format has seventeen keypoints in a specific order. PersonLimb format is different. If you switch backbones mid-project without checking the output schema, your training code will silently accept the wrong indices and you'll waste hours debugging weird loss curves. I learned this the hard way when I swapped from a pretrained model to a finetuned variant without verifying the keypoint mapping. The model trained fine for three epochs and then the mAP dropped to zero. Turns out the finetuned model had flipped the left and right hand indices. There's a keypoint remapping flag in the latest version but it's buried in the config file.

Performance Considerations

Processing speed depends heavily on your hardware and the model you choose. On a single RTX 3090, running YOLOv8-Pose on a folder of 1920x1080 images at default settings takes roughly two to three seconds per image. That's with batch processing enabled. Without batching, it's closer to four or five seconds per image because of per-frame overhead. If you're processing thousands of images, the difference between batch mode and single-image mode is significant. I ran a test on a 2000-image dataset. Batch mode completed in about ninety minutes. Single-image mode took roughly four and a half hours. The improvement isn't linear because GPU memory and dispatch overhead play a role, but it's substantial enough that you should always use batching for anything larger than a hundred images. CPU-only processing is possible but painfully slow. Don't bother unless you're just testing the pipeline on a handful of images. On a recent Ryzen 9 with no GPU, the same 2000-image set took about eight hours. That's not a recommendation, just a data point.

Export Formats And Integration

The tool supports JSON, CSV, and format directly compatible with some popular training frameworks. The JSON output includes metadata like image path, person count, confidence scores, and keypoint arrays. The CSV version is simpler — one row per keypoint per person, which makes it easier to load into pandas or sqlite if you need to do post-processing. For training integration, I recommend converting the exported data into whatever format your framework expects rather than feeding the raw output directly. The extra step saves you from mismatched schemas later. I wrote a small conversion script that reads the JSON and outputs YOLOv8 pose format with segment and keypoint fields. Took about two hours to write and debug, but it eliminated an entire class of errors during training setup. If you need a download link, the project is on GitHub at Ultralytics' repository. Search for Skeletons Out Of The Closet on their releases page. The latest stable build includes bug fixes for the multi-person tracking inconsistency I mentioned earlier, so make sure you're not running an old version.

When It Doesn't Work

The tool assumes your input images contain detectable human figures. If your dataset is mostly empty frames, blurred shots, or unconventional subjects like mannequins or cartoons, the pose estimator will either return nothing or produce garbage keypoints. There's no automatic quality filter for this beyond the confidence threshold, and that threshold only filters individual detections, not entire frames. I processed a dataset of stage performances where the lighting made skin tones nearly invisible to the model. The confidence scores looked fine on paper but the actual keypoint positions were wildly inaccurate. I had to write a post-processing pass that validated keypoint geometry against expected limb lengths before accepting any annotation. That added maybe twenty minutes to an otherwise quick pipeline run, but it prevented months of bad training data. Another limitation is that the tool doesn't handle extreme aspect ratios well. Images wider than 16:9 or taller than 4:3 sometimes get misaligned during the normalization step depending on the model. I've seen bounding boxes snap to incorrect positions when the input was rotated or cropped unusually. The workaround is to preprocess your images into a standard aspect ratio before running them through the pipeline.

Alternatives And When To Use Them

If you only need basic pose extraction and don't care about the full curation workflow, MediaPipe Pose is faster to set up and handles most everyday cases adequately. It's also lighter on resources. But if you're building a production pipeline and need consistent output formatting, metadata handling, and integration with training frameworks, Skeletons Out Of The Closet is worth the initial friction. For video-specific projects where temporal consistency matters more than per-frame accuracy, I'd look at tools built around DeepLabCut or similar academic frameworks. They're slower and harder to install but they handle tracking across frames natively instead of requiring you to bolt on a separate tracking module. The bottom line is that this tool does what it claims to do, but the claims assume a level of familiarity with pose estimation pipelines that most beginners don't have. Read the config options carefully, validate your outputs visually before training, and don't skip the post-processing step even if your dataset looks clean at first glance. I wish I'd known that before spending a week chasing down errors that came from bad annotations, not bad model weights.