  HybrIK relies on a combination of analytical inverse kinematics and deep
learning to produce more accurate 3D pose estimation from 2D monocular images.
HybrIK has three major components: (1) pretrained convolution backbone, (2)
deconvolution to lift 3D pose from 2D convolution features, (3) analytical
inverse kinematics pass correcting deep learning prediction using learned
distribution of plausible twist and swing angles. In this paper we propose an
enhancement of the 2D to 3D lifting module, replacing deconvolution with
Transformer, resulting in accuracy and computational efficiency improvement
relative to the original HybrIK method. We demonstrate our results on commonly
used H36M, PW3D, COCO and HP3D datasets. Our code is publicly available
https://github.com/boreshkinai/hybrik-transformer.
