Use the done() tool to submit your answer.<｜DSML｜tool_calls> <｜DSML｜invoke name="done"> <｜DSML｜parameter name="result" string="true">## Latest Competitors to OpenPose (as of mid-2026) OpenPose (originally released in 2017 by CMU) is no longer the state-of-the-art in pose estimation. Several modern alternatives have surpassed it in accuracy, speed, and ease of deployment. Based on current sources, here are the **latest and most notable competitors**: ### 1. **YOLO11 Pose** (Ultralytics, 2024–2025) - **The strongest all-around competitor today.** YOLO11 Pose is the pose estimation variant of Ultralytics' YOLO11 object detection model. It offers the best balance of accuracy and speed for most applications, supports multi-person pose estimation, and runs in real-time on both GPU and edge devices. It is widely considered the top choice for new projects in 2026. ### 2. **MediaPipe Pose (BlazePose)** (Google) - **Best for mobile and web.** Google's MediaPipe framework includes BlazePose, which is lightweight, runs on-device (CPU/GPU), and supports 33 keypoints. It's the go-to for mobile apps, fitness tracking, and browser-based applications. It's faster than OpenPose but slightly less accurate on complex poses. ### 3. **RTMPose** (OpenMMLab / MMPose) - **Best for high-accuracy real-time applications.** RTMPose is a family of models from the MMPose toolkit that achieves state-of-the-art speed-accuracy trade-offs. It outperforms OpenPose significantly on standard benchmarks (COCO, MPII) while running at real-time speeds. It's a direct, modern replacement for OpenPose in production pipelines. ### 4. **ViTPose** (Vision Transformer-based) - **Best for maximum accuracy (non-real-time).** ViTPose uses Vision Transformer (ViT) backbones and achieves the highest accuracy on pose estimation benchmarks. It's heavier and slower than CNN-based models, so it's best for offline or server-side processing where accuracy is paramount. ### 5. **MoveNet** (Google / TensorFlow) - **Best for ultra-lightweight edge deployment.** MoveNet (Lightning & Thunder variants) is optimized for mobile and IoT devices. It's less accurate than YOLO11 Pose or RTMPose but is extremely fast and efficient on low-power hardware. ### 6. **DWPose** (Depth-aware pose estimation) - A newer approach that incorporates depth information for more robust pose estimation, particularly useful when body parts are occluded. ### 7. **AlphaPose** - An older but still maintained alternative that focuses on high-precision multi-person pose estimation. It's been largely superseded by RTMPose and YOLO11 Pose in terms of speed. ### 8. **DensePose** (Facebook/Meta) - Maps all human pixels to a 3D surface model of the body, providing much richer information than OpenPose's 17 keypoints. However, it's significantly slower (seconds per image). --- ### Summary: Which one is the "latest competitor"? If you're asking for the **single latest competitor that has most directly replaced OpenPose**, the answer is **YOLO11 Pose** (from Ultralytics, released in late 2024/2025). It's the most actively developed, best-performing, and easiest-to-deploy option as of mid-2026. For real-time production pipelines, **RTMPose** (from MMPose/OpenMMLab) is the closest direct replacement with the best speed-accuracy trade-off. For mobile/edge, **MediaPipe Pose** remains the dominant choice. *Sources: Roboflow blog (Apr 2026), Forasoft pose-tracking stack analysis (May 2026), Kite Metric comparison, GitHub comparison projects, and multiple 2025–2026 academic surveys.*</｜DSML｜parameter> </｜DSML｜invoke> </｜DSML｜tool_calls>