3D human shape and pose estimation is the essential task for human motion
analysis, which is widely used in many 3D applications. However, existing
methods cannot simultaneously capture the relations at multiple levels,
including spatial-temporal level and human joint level. Therefore they fail to
make accurate predictions in some hard scenarios when there is cluttered
background, occlusion, or extreme pose. To this end, we propose Multi-level
Attention Encoder-Decoder Network (MAED), including a Spatial-Temporal Encoder
(STE) and a Kinematic Topology Decoder (KTD) to model multi-level attentions in
a unified framework. STE consists of a series of cascaded blocks based on
Multi-Head Self-Attention, and each block uses two parallel branches to learn
spatial and temporal attention respectively. Meanwhile, KTD aims at modeling
the joint level attention. It regards pose estimation as a top-down
hierarchical process similar to SMPL kinematic tree. With the training set of
3DPW, MAED outperforms previous state-of-the-art methods by 6.2, 7.2, and 2.4
mm of PA-MPJPE on the three widely used benchmarks 3DPW, MPI-INF-3DHP, and
Human3.6M respectively. Our code is available at
this https URL

提出了一种基于多级注意力编码器 - 解码器网络（MAED）的 3D 人形状和姿势估计方法，其中包括空间 - 时间编码器（STE）和运动学拓扑解码器（KTD），并考虑了多级注意力，从而优于现有方法。