DWPoseVSOpenPose
We compare the two most-used pose estimation preprocessors in Stable Diffusion ControlNet workflows: the modern DWPose and the established OpenPose. We explain which to choose in terms of accuracy (especially hand detection), whole-body coverage, speed, license, and ControlNet usage.
Tool Overview
DWPose
Evaluate DWPose as a pose preprocessor: separate detection, keypoint mapping, rendered pose maps and downstream generation before judging a character-reference workflow.
OpenPose
OpenPose is the pioneering real-time multi-person pose estimation system developed at Carnegie Mellon University that simultaneously detects body, face, hand, and foot keypoints of multiple people in images and videos. As the first open-source system to achieve real-time multi-person pose detection, OpenPose has become a foundational tool in computer vision research and creative AI applications. Built on a CNN (Convolutional Neural Network) architecture with approximately 25 million parameters, the model uses Part Affinity Fields (PAFs) to associate detected body parts with the correct individuals in crowded scenes, enabling accurate pose estimation even when people overlap or partially occlude each other. OpenPose detects up to 135 keypoints per person covering the full body skeleton with 25 points, each hand with 21 points, and the face with 70 points, providing comprehensive pose information for detailed motion analysis. The system processes both images and video streams, delivering real-time performance on modern GPUs that makes it suitable for interactive applications. OpenPose has been extensively integrated into AI image generation workflows, particularly as the standard pose extraction method for ControlNet conditioning in Stable Diffusion and FLUX-based generation pipelines. Released under a custom non-commercial license, the source code is available on GitHub and has accumulated one of the highest star counts among computer vision repositories. Key applications include motion capture for animation and gaming, fitness and rehabilitation tracking, sports biomechanics analysis, sign language recognition, dance analysis, human-computer interaction research, and providing pose conditioning for AI image generation tools.
Detailed Comparison
Scores and notes are stored editorial judgements, not user ratings or independently reproduced benchmarks. Verify current provider claims before deciding.
Swipe across to compare all tools.
| Feature | DWPose | OpenPose |
|---|---|---|
| Overall Accuracy (whole-body) | 5/5 DWPose-l ~66.5% whole-body AP (COCO-WholeBody); improved through distillation | 3/5 Groundbreaking but older; significantly lower whole-body AP |
| Hand Detection | 5/5 Clear advantage with hands; its strongest aspect in ControlNet | 3/5 Frequently misses hands; coarse finger detail in the full-body map |
| Whole-Body Coverage | 5/5 133 keypoints: body, feet, face, hands | 4/5 Supports 135 keypoints (body/feet/face/hands) |
| ControlNet Usage | 5/5 dw_openpose_full preprocessor; recommended for accuracy in ComfyUI/A1111 | 4/5 Original OpenPose preprocessor; broad support and editing tools |
| Speed | 4/5 Efficient, RTMPose-based; generally faster inference than OpenPose | 3/5 Requires a GPU for high performance |
| License (Commercial Use) | 5/5 Apache-2.0 — commercial use allowed | 2/5 Noncommercial/academic license; restricted commercial use |
| Total | 29/30 | 19/30 |
Pros & Cons
DWPose
Evaluate DWPose as a pose preprocessor: separate detection, keypoint mapping, rendered pose maps and downstream generation before judging a character-reference workflow.
Pros
- The inspected implementation makes the detector and pose-estimator stages separately identifiable.
- The pose estimator returns coordinates and confidence scores before the drawing wrapper creates the guide image.
- The reviewed official code and exact ONNX checkpoint repository declare Apache 2.0.
Cons
- A correct pose map does not guarantee that a downstream image generator will follow it.
- Low-confidence suppression, remapping and drawing choices can make the visible map differ from raw whole-body output.
- The inspected wrapper contains CUDA-specific behavior and does not prove compatibility or speed for every integration.
OpenPose
OpenPose is the pioneering real-time multi-person pose estimation system developed at Carnegie Mellon University that simultaneously detects body, face, hand, and foot keypoints of multiple people in images and videos. As the first open-source system to achieve real-time multi-person pose detection, OpenPose has become a foundational tool in computer vision research and creative AI applications. Built on a CNN (Convolutional Neural Network) architecture with approximately 25 million parameters, the model uses Part Affinity Fields (PAFs) to associate detected body parts with the correct individuals in crowded scenes, enabling accurate pose estimation even when people overlap or partially occlude each other. OpenPose detects up to 135 keypoints per person covering the full body skeleton with 25 points, each hand with 21 points, and the face with 70 points, providing comprehensive pose information for detailed motion analysis. The system processes both images and video streams, delivering real-time performance on modern GPUs that makes it suitable for interactive applications. OpenPose has been extensively integrated into AI image generation workflows, particularly as the standard pose extraction method for ControlNet conditioning in Stable Diffusion and FLUX-based generation pipelines. Released under a custom non-commercial license, the source code is available on GitHub and has accumulated one of the highest star counts among computer vision repositories. Key applications include motion capture for animation and gaming, fitness and rehabilitation tracking, sports biomechanics analysis, sign language recognition, dance analysis, human-computer interaction research, and providing pose conditioning for AI image generation tools.
Pros
- Real-time multi-person pose estimation — body, hand, and face keypoints
- Pioneering open-source pose estimation library developed by CMU
- Widespread integration with ControlNet — standard tool in AI image generation
- Scalable multi-person support with bottom-up approach
Cons
- Surpassed in accuracy by DWPose and other newer models
- Complex installation — Caffe and CUDA dependencies
- Poor real-time performance without GPU
Editorial note
Authored conclusion · not independently tested
Overall winner: DWPose. DWPose is a modern whole-body pose estimator that improves the RTMPose backbone with two-stage distillation and offers high accuracy on COCO-WholeBody (DWPose-l around 66.5% whole-body AP). Its clearest advantage is hand detection: the ControlNet community reports that DWPose (dw_openpose_full) is noticeably more accurate than OpenPose on hands; where standard OpenPose misses a hand, DWPose captures both. It is also Apache 2.0 licensed. OpenPose is CMU's pioneering real-time multi-person system and is still widely used, but it is older technology, weaker on hands, and carries an important constraint: its non-commercial/academic license restricts commercial use. For the highest pose and hand accuracy in ControlNet, prefer DWPose; for a mature, lightweight baseline, OpenPose can still serve — but most modern workflows now recommend DWPose.