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方法结合解析逆运动学与深度学习，从二维单目图像中实现更精确的三维姿态估计。该方法包含三大核心模块：（1）预训练卷积骨干网络，（2）通过反卷积从二维卷积特征中提升三维姿态，（3）利用合理扭转与摆角的学习分布修正深度学习预测的解析逆运动学通路。本文提出对二维到三维提升模块的改进方案，使用Transformer替代反卷积操作，在保持原HybrIK方法精度的同时提升计算效率。我们在H36M、PW3D、COCO及HP3D等常用数据集上验证了结果，相关代码已在https://github.com/boreshkinai/hybrik-transformer 开源。