pose-estimationProvides 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.
Independent GitHub snapshot · Automated classification · Original documentation governs
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.
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.
init_pose_estimator / inference_pose_estimator APIs; read
api-reference.pose_demo (Cascade-RCNN + HRNet) or pose_demo_HD (HTC + HRNet); read
workflows.processor.skeleton_dataset.build configuration flow. tracker_cfg must be
null; a non-null tracker raises NotImplementedError in the builder.