If you have used pose control in Stable Diffusion for a while, you probably started with OpenPose, the estimator that defined the category. Then dwpose showed up inside ComfyUI and AUTOMATIC1111, and suddenly the skeletons had working fingers. So which one should you actually use now? This comparison walks through both tools honestly: where each one came from, how their keypoints stack up, how they handle nasty poses, what they cost you in speed and setup effort, and the situations where the older tool still earns its place. By the end you will know exactly which estimator belongs in your workflow, and it may not be the same answer for every project.
Where Each Tool Comes From
OpenPose is the veteran. It was built as a research framework in C++, became the standard way to extract body keypoints from photos, and its skeleton style became the language that ControlNet’s OpenPose models learned to read. When people say “an OpenPose map” today, they usually mean that skeleton format, regardless of which program drew it. DWPose is the challenger: a 2023 whole-body model from IDEA-Research, trained with a two-stage distillation method on top of the MMPose and RTMPose ecosystem, designed from the start to be both accurate and quick. One tool defined the format; the other arrived to produce that format better.
Keypoint Coverage: The Biggest Gap
DWPose marks 133 keypoints in the COCO-WholeBody layout: 17 body, 6 feet, 68 face and 42 hand points, 21 for each hand. That hand and face coverage is the headline difference. Classic OpenPose setups give you solid body joints, but hands were historically handled by a separate, fussier detection stage, and in many ControlNet-era workflows people simply used body-only skeletons with a dot or a rough blob where each hand should be. The result showed up in generations: characters whose arms posed beautifully and whose hands did whatever they wanted. Dwpose captures finger positions in the same single pass as the body, so a thumbs-up, a grip on an imaginary sword or a delicate pinch all survive the trip from photo to skeleton to generated image.
Accuracy on Difficult Poses
Real reference photos are rarely tidy. People twist, crouch, lean out of frame, cross their arms, or get half hidden behind furniture. This is where dwpose earns its reputation. Its training and design make it more robust on difficult poses and partial bodies: it holds onto wrists and ankles that OpenPose tends to drop, guesses less wildly when a limb is foreshortened, and produces steadier face landmarks when the head is tilted or turned. OpenPose is still respectable on clean, upright, fully visible subjects. The gap opens up exactly when your references get interesting, which for creative work is most of the time.
Speed and Hardware Cost
DWPose was distilled into a small, fast student model, and it shows. Running through ONNX Runtime, it estimates a pose quickly, and on an NVIDIA GPU it feels close to real time; even on CPU it finishes a still image in seconds. OpenPose, the full framework, is heavier: a bigger install, more moving parts, and slower going on modest machines unless you configure it carefully. Inside ComfyUI the practical difference is simple. Dwpose arrives inside a node pack and just runs. Getting classic OpenPose running standalone is a project of its own, which is why the preprocessors bundled in creative tools switched their default to dwpose.
Setup and Everyday Convenience
- DWPose: install the comfyui_controlnet_aux pack in ComfyUI, or use the built-in preprocessor in the A1111 ControlNet extension. Models download automatically. Done.
- OpenPose: the original framework expects a manual install with its own dependencies. The editors and preprocessors that carried its name in creative tools still exist, but the ecosystem has largely moved on.
Convenience compounds over time. Because dwpose is maintained inside the node packs and extensions people already update, fixes and improved model options (including newer RTMW-based choices) reach you through your normal update routine.
Compatibility: The Plot Twist
Here is the part that confuses newcomers, and it is good news. DWPose draws its skeletons in the OpenPose style, which means the ControlNet OpenPose models you already own accept dwpose output without any conversion. Choosing dwpose does not lock you out of anything or force you to replace your ControlNet models. You are swapping the measurer, not the language it speaks. Your existing workflows, saved skeletons and OpenPose control models all keep working; they simply get fed better maps.
That compatibility also lowers the risk of trying dwpose to nearly zero. You can run both estimators on the same reference photo inside the same session, compare the two skeletons side by side, and keep whichever one captured the pose more faithfully. Most people do this comparison exactly once, on a photo with visible hands, and then quietly change their default.
So, Which One Should You Use?
For AI image generation with ControlNet, dwpose is the right default for nearly everyone: better hands, better faces, steadier results on hard poses, faster runs and a far easier install. OpenPose still has a place if you are maintaining an older pipeline built around it, doing research that specifically compares against it, or working in a tool that only ships the classic estimator. Even then, many people run dwpose for production work and keep OpenPose around as a reference point. Pick per project if you must, but know that the default has genuinely changed.
Face and Expression Work
The 68 face landmarks deserve their own mention, because they quietly change what pose control can do. A classic body skeleton tells ControlNet where the head is and roughly which way it faces. Dwpose’s facial points describe the tilt, turn and proportions of the face itself, so generated portraits follow the reference’s head angle and expression structure far more closely. If your work involves character portraits, reaction shots or matching a specific person’s head pose across a series, this alone justifies the switch. OpenPose-era workflows simply had nothing equivalent feeding the control model, and it showed in how often heads drifted into a generic forward-facing pose.
Community Momentum and Maintenance
Tools improve at the speed of their communities, and here the momentum sits with dwpose. The comfyui_controlnet_aux pack and the A1111 ControlNet extension both treat dwpose as the primary pose preprocessor, which means bug fixes, performance work and new model options land there first. Newer RTMW-based model choices have already appeared inside the node pack for people who want them. OpenPose the framework is mature and stable, but stable in this context mostly means finished; the creative-tooling world has picked its default and moved on. Choosing the actively maintained option protects your workflow from slowly bit-rotting as ComfyUI and the extensions evolve around it.
Conclusion
OpenPose invented the game, and its skeleton format is still the standard that ControlNet speaks. Dwpose simply plays that game better where it counts: 133 keypoints with real finger and face detail, stronger results on difficult and partial poses, quicker estimation and a setup that takes minutes inside the tools you already use. Unless you have a specific legacy reason to stay, make dwpose your pose estimator and let OpenPose enjoy its retirement as a file format.
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