Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose Video Inference for Body Pose and Shape Estimation (VIBE), which makes use of an existing large-scale motion capture dataset (AMASS) together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty is an adversarial learning framework that leverages AMASS to discriminate between real human motions and those produced by our temporal pose and shape regression networks. We define a temporal network architecture and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels. We perform extensive experimentation to analyze the importance of motion and demonstrate the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving state-of-the-art performance. Code and pretrained models are available at https://github.com/mkocabas/VIBE.

本文提出了基于视频的身体姿态和形状估计方法(VIBE)，利用现有的大规模运动捕捉数据集(AMASS)和无配对的野外2D关键点注释，通过对抗学习框架，利用AMASS区分真实人体运动和由时态姿态和形状回归网络生成的运动，得到运动检测序列，具有动力学合理性，成功地解决了基于视频的人体三维姿态估计的挑战问题。

VIBE：基于视频推导的人体姿态和形状估计