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.

翻译：HybrIK结合了解析逆运动学与深度学习，以从2D单目图像中更精确地估计3D人体姿态。HybrIK包含三个主要组件：（1）预训练卷积骨干网络；（2）通过反卷积从2D卷积特征中提升3D姿态；（3）利用合理扭转角和摆动角的习得分布对深度学习预测进行校正的解析逆运动学传递过程。本文提出对2D到3D提升模块的改进，用Transformer替代反卷积，从而在原始HybrIK方法的基础上提升精度与计算效率。我们在广泛使用的H36M、PW3D、COCO和HP3D数据集上验证了结果。代码已开源：https://github.com/boreshkinai/hybrik-transformer。