A ComfyUI custom node that rotates OpenPose figures around their torso pivot point. The node accepts an image and optional pose keypoints, detects the torso, applies Y-axis rotation (left/right), and outputs the rotated pose image.

Features

Input: Image (required) + optional POSE_KEYPOINT from OpenPose/DWPose preprocessors
Parameters: Direction (left/right), degrees (1-360)
Fallback: When POSE_KEYPOINT is not provided, uses DWPose from comfyui-controlnet-aux to extract keypoints (requires that extension)
Error handling: Returns input image unchanged if torso cannot be detected
Anatomy-aware rotation: Limb-specific depth scales per OpenPose COCO body indices for more natural turns
Adaptive depth: Depth scale adapts to shoulder width for consistent results across image sizes
Face visibility: Face/head keypoints (nose, eyes, ears) hidden when rotation >90° (back of head)
Occlusion: Points behind the torso are hidden; limbs drawn in depth order for correct overlap

Installation

Clone or copy this folder into your ComfyUI custom_nodes directory. Use a valid Python module name (underscores, not hyphens):
```
ComfyUI/custom_nodes/open_pose_rotator/
```
(If your folder is open-pose-rotator, rename it to open_pose_rotator so Python can import it.)
Install dependencies:
```
pip install -r requirements.txt
```
(Optional, for image-only input) Install comfyui-controlnet-aux for DWPose fallback when pose keypoints are not connected.

Usage

Add OpenPose Rotator from the node menu under image/pose
Connect an OpenPose/DWPose image to the image input
Optionally connect POSE_KEYPOINT from DWPose Estimator or OpenPose Pose for better accuracy
Set direction (left = counterclockwise, right = clockwise when viewed from above)
Set degrees (1-360)

Workflow Example

With keypoints: Load Image → DWPose Estimator → OpenPose Rotator (connect both IMAGE and POSE_KEYPOINT) → Apply ControlNet
Image only: Load Image → OpenPose Rotator → Apply ControlNet (requires comfyui-controlnet-aux for DWPose)

Implementation Notes

Rotation uses geometry-based depth inference from 2D OpenPose keypoints (no ML dependency). Depth is inferred per OpenPose COCO body index using anatomic proportions. For true 3D pose lifting from 2D, libraries like VideoPose3D or MMPose exist but use different keypoint formats (e.g. Human3.6M) and would require format conversion.

License

MIT