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We present the first real-time method to capture the full global 3D skeletalpose of a human in a stable, temporally consistent manner using a single RGBcamera. Our method combines a new convolutional neural network (CNN) based poseregressor with kinematic skeleton fitting. Our novel fully-convolutional poseformulation regresses 2D and 3D joint positions jointly in real time and doesnot require tightly cropped input frames. A real-time kinematic skeletonfitting method uses the CNN output to yield temporally stable 3D global posereconstructions on the basis of a coherent kinematic skeleton. This makes ourapproach the first monocular RGB method usable in real-time applications suchas 3D character control---thus far, the only monocular methods for suchapplications employed specialized RGB-D cameras. Our method's accuracy isquantitatively on par with the best offline 3D monocular RGB pose estimationmethods. Our results are qualitatively comparable to, and sometimes betterthan, results from monocular RGB-D approaches, such as the Kinect. However, weshow that our approach is more broadly applicable than RGB-D solutions, i.e. itworks for outdoor scenes, community videos, and low quality commodity RGBcameras.

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera