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3D Human Pose Demo


3D Human Pose Two-stage Estimation Demo

Using mmdet for human bounding box detection and top-down model for the 1st stage (2D pose detection), and inference the 2nd stage (2D-to-3D lifting)

Assume that you have already installed mmdet.

python  demo/body3d_pose_lifter_demo.py  \
${MMDET_CONFIG_FILE} \
${MMDET_CHECKPOINT_FILE} \
${MMPOSE_CONFIG_FILE_2D} \
${MMPOSE_CHECKPOINT_FILE_2D} \
${MMPOSE_CONFIG_FILE_3D} \
${MMPOSE_CHECKPOINT_FILE_3D} \
--input ${VIDEO_PATH or IMAGE_PATH or 'webcam'} \
[--show] \
[--disable-rebase-keypoint] \
[--disable-norm-pose-2d] \
[--num-instances ${NUM_INSTANCES}] \
[--output-root ${OUT_VIDEO_ROOT}] \
[--save-predictions] \
[--device ${GPU_ID  or  CPU}] \
[--det-cat-id ${DET_CAT_ID}] \
[--bbox-thr ${BBOX_THR}] \
[--kpt-thr ${KPT_THR}] \
[--use-oks-tracking] \
[--tracking-thr ${TRACKING_THR}] \
[--show-interval ${INTERVAL}] \
[--thickness ${THICKNESS}] \
[--radius ${RADIUS}] \
[--online]

Note that

  1. ${VIDEO_PATH} can be the local path or URL link to video file.

  2. If the [--online] option is set to True, future frame information can not be used when using multi frames for inference in the 2D pose detection stage.

Examples:

During 2D pose detection, for single-frame inference that do not rely on extra frames to get the final results of the current frame and save the prediction results, try this:

python demo/body3d_pose_lifter_demo.py  \
demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth \
configs/body_3d_keypoint/video_pose_lift/h36m/video-pose-lift_tcn-243frm-supv-cpn-ft_8xb128-200e_h36m.py \
https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth  \
--input https://user-images.githubusercontent.com/87690686/164970135-b14e424c-765a-4180-9bc8-fa8d6abc5510.mp4 \
--output-root  vis_results \
--save-predictions

During 2D pose detection, for multi-frame inference that rely on extra frames to get the final results of the current frame, try this:

python demo/body3d_pose_lifter_demo.py \
demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \
configs/body_2d_keypoint/rtmpose/body8/rtmpose-m_8xb256-420e_body8-256x192.py \
https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth \
configs/body_3d_keypoint/video_pose_lift/h36m/video-pose-lift_tcn-243frm-supv-cpn-ft_8xb128-200e_h36m.py \
https://download.openmmlab.com/mmpose/body3d/videopose/videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth \
--input https://user-images.githubusercontent.com/87690686/164970135-b14e424c-765a-4180-9bc8-fa8d6abc5510.mp4 \
--output-root  vis_results  \
--online

3D Human Pose Demo with Inferencer

The Inferencer provides a convenient interface for inference, allowing customization using model aliases instead of configuration files and checkpoint paths. It supports various input formats, including image paths, video paths, image folder paths, and webcams. Below is an example command:

python demo/inferencer_demo.py tests/data/coco/000000000785.jpg \
    --pose3d human3d --vis-out-dir vis_results/human3d

This command infers the image and saves the visualization results in the vis_results/human3d directory.

Image 1

In addition, the Inferencer supports saving predicted poses. For more information, please refer to the inferencer document.