  Estimating robot pose and joint angles is significant in advanced robotics,
enabling applications like robot collaboration and online hand-eye
calibration.However, the introduction of unknown joint angles makes prediction
more complex than simple robot pose estimation, due to its higher
dimensionality.Previous methods either regress 3D keypoints directly or utilise
a render&compare strategy. These approaches often falter in terms of
performance or efficiency and grapple with the cross-camera gap problem.This
paper presents a novel framework that bifurcates the high-dimensional
prediction task into two manageable subtasks: 2D keypoints detection and
lifting 2D keypoints to 3D. This separation promises enhanced performance
without sacrificing the efficiency innate to keypoint-based techniques.A vital
component of our method is the lifting of 2D keypoints to 3D keypoints. Common
deterministic regression methods may falter when faced with uncertainties from
2D detection errors or self-occlusions.Leveraging the robust modeling potential
of diffusion models, we reframe this issue as a conditional 3D keypoints
generation task. To bolster cross-camera adaptability, we introduce
theNormalised Camera Coordinate Space (NCCS), ensuring alignment of estimated
2D keypoints across varying camera intrinsics.Experimental results demonstrate
that the proposed method outperforms the state-of-the-art render\&compare
method and achieves higher inference speed.Furthermore, the tests accentuate
our method's robust cross-camera generalisation capabilities.We intend to
release both the dataset and code in https://nimolty.github.io/Robokeygen/
