HumanoidPose:Monocular 3D Pose Estimation for Humanoid Robots

As humanoid robots increasingly enter real-world environments, recovering their 3D whole-body pose from monocular imagery is becoming increasingly important for external state perception, motion analysis, and robot learning. However, Humanoid Robot Pose Estimation (HRPE) lies at the intersection of conventional Robot Pose Estimation (RPE) and Human Pose Estimation (HPE), while not being fully addressed by either paradigm: RPE primarily focuses on non-humanoid robots, particularly fixed-base manipulators, whereas HPE is built upon human-specific skeletal topologies, body models, and motion priors. Despite recent progress in humanoid 2D keypoint detection and HPE adaptation, humanoid-native 3D pose recovery remains insufficiently studied, and a dedicated benchmark with suitable training data and evaluation protocols is still lacking. To address this gap, we introduce Humanoids, a benchmark for monocular 3D humanoid robot pose estimation, and propose HumanoidPose, a framework for this task. Humanoids provides synthetic 3D supervision and real-world evaluation across diverse humanoid embodiments, motions, and visual conditions, while HumanoidPose combines human-inspired visual perception with robot articulation modeling and explicit kinematic grounding. Experiments on Humanoids and the DHRP-H1 benchmark show that HumanoidPose outperforms representative HPE and RPE approaches in articulation recovery, 3D pose estimation, and real-world evaluation. Ablation studies further demonstrate the contribution of robot-native articulation modeling and explicit kinematic grounding. The dataset and code will be publicly released to support future research in the community.