We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal
pose and shape. Though substantial progress has been made in estimating 3D
human motion and shape from dynamic observations, recovering plausible pose
sequences in the presence of noise and occlusions remains a challenge. For this
purpose, we propose an expressive generative model in the form of a conditional
variational autoencoder, which learns a distribution of the change in pose at
each step of a motion sequence. Furthermore, we introduce a flexible
optimization-based approach that leverages HuMoR as a motion prior to robustly
estimate plausible pose and shape from ambiguous observations. Through
extensive evaluations, we demonstrate that our model generalizes to diverse
motions and body shapes after training on a large motion capture dataset, and
enables motion reconstruction from multiple input modalities including 3D
keypoints and RGB(-D) videos.

引入了 HuMoR：一种用于鲁棒估计时间姿态和形状的 3D 人体运动模型。该模型是一个表达能力强的条件变分自编码器生成模型，学习了运动序列每一步姿态变化的分布。通过灵活的优化方法，可以利用 HuMoR 运动先验知识从模糊的观察中鲁棒地估计出合理的姿态和形状。经过广泛的评估，证明我们的模型在大量运动捕捉数据集上训练后，可以推广到多样化的动作和身体形状，并且可以从多种输入模态（包括 3D 关键点和 RGB (-D) 视频）重建运动。