Learning Joint Twist Rotation for 3D Human Pose Estimation from a Single Image

We consider monocular 3D human pose estimation with joint rotation representations because they are more expressive than joint location representations and better suited to some applications such as CG character control and human motion analysis in sports or surveillance. Previous approaches have encountered difficulties when estimating joint rotations with actual twist rotation around limbs. We present a novel approach to estimating joint rotations with actual twist rotations from a single image by handling joint rotations separately decomposed into swing and twist rotations. To extract twist rotations from an image, we emphasize the joint appearances and use them effectively in our model. Our model estimates the twist angles with an average radian error of 0.14, and we show that estimation of twist rotations achieves a more precise 3D human pose.