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name: pose-estimation description: "Provides optional image and video pose-estimation workflows with cascade-RCNN or HTC detection, HRNet keypoints, skeleton-dataset handoff, and explicit detector-readiness gates for mmskeleton users." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pose estimation

Use this sub-skill for pose_demo, pose_demo_HD, image pose inference, video-to-skeleton dataset building, Cascade-RCNN or HTC plus HRNet configuration, and handing pose output to recognition. This is an optional detector-backed path, not proof that the verified ST-GCN core can execute a detector.

Gate first

Run the bundled no-download checker from any working directory when checking an environment:

python /path/to/this-skill/scripts/check_pose_readiness.py --device cuda
python /path/to/this-skill/scripts/check_pose_readiness.py --device cuda --require-detector

Use the second form only when detector execution is required. It returns nonzero when the detector stack is absent or incomplete; without --require-detector, the checker is informational and returns zero. The checker never downloads weights and does not run an image, video, or detector workflow. See compatibility and troubleshooting.

The current evidence permits the following conservative statement: torch's CUDA core works in the prepared environment, but the MMDetection workflow is unverified. Lightweight MMCV lacks mmcv._ext, and an attempted mmcv-full==1.7.2 source build failed because thrust/complex.h was missing. MMDetection workflows are therefore optional and unresolved. Do not infer detector readiness from a successful ST-GCN smoke.

Route

• For a single image or frame and a Python result, use the documented init_pose_estimator / inference_pose_estimator APIs; read api-reference.
• For sequential video inference and an optional rendered output video, use pose_demo (Cascade-RCNN + HRNet) or pose_demo_HD (HTC + HRNet); read workflows.
• For a directory of videos and recognition-ready JSON files, use the processor.skeleton_dataset.build configuration flow. tracker_cfg must be null; a non-null tracker raises NotImplementedError in the builder.
• Send produced JSON to data-preparation for validation, then send validated skeleton data to recognition. Do not make this sub-skill the JSON validator or the ST-GCN recognition guide.

Read next

• API and output contract
• CLI and dataset workflows
• dependency, hardware, and checkpoint compatibility
• failure handling and unresolved limits