For the current 3D human pose estimation task, a group of methods mainly
learn the rules of 2D-3D projection from spatial and temporal correlation.
However, earlier methods model the global features of the entire body joint in
the time domain, but ignore the motion trajectory of individual joint. The
recent work [29] considers that there are differences in motion between
different joints and deals with the temporal relationship of each joint
separately. However, we found that different joints show the same movement
trends under some specific actions. Therefore, our proposed Fusionformer method
introduces a self-trajectory module and a mutual-trajectory module based on the
spatio-temporal module .After that, the global spatio-temporal features and
local joint trajectory features are fused through a linear network in a
parallel manner. To eliminate the influence of bad 2D poses on 3D projections,
finally we also introduce a pose refinement network to balance the consistency
of 3D projections. In addition, we evaluate the proposed method on two
benchmark datasets (Human3.6M, MPI-INF-3DHP). Comparing our method with the
baseline method poseformer, the results show an improvement of 2.4% MPJPE and
4.3% P-MPJPE on the Human3.6M dataset, respectively.

提出了一种名为 Fusionformer 的方法用于 3D 人体姿态估计任务，通过引入自身轨迹模块、互相轨迹模块和全局空间时间特征以及局部关节轨迹特征的融合，最终通过姿势精炼网络来平衡 3D 投影的一致性，并在两个基准数据集上进行评估，结果表明与基线方法 poseformer 相比，在 Human3.6M 数据集上分别提高了 2.4％的 MPJPE 和 4.3％的 P-MPJPE。

利用基于 Transformer 的融合网络探索联合动作协同性用于 3D 人体姿态估计

(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation

Multi-person pose estimation in images and videos is an important yet
challenging task with many applications. Despite the large improvements in
human pose estimation enabled by the development of convolutional neural
networks, there still exist a lot of difficult cases where even the
state-of-the-art models fail to correctly localize all body joints. This
motivates the need for an additional refinement step that addresses these
challenging cases and can be easily applied on top of any existing method. In
this work, we introduce a pose refinement network (PoseRefiner) which takes as
input both the image and a given pose estimate and learns to directly predict a
refined pose by jointly reasoning about the input-output space. In order for
the network to learn to refine incorrect body joint predictions, we employ a
novel data augmentation scheme for training, where we model "hard" human pose
cases. We evaluate our approach on four popular large-scale pose estimation
benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack
Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement
over the state of the art.

本文介绍了一种姿势细化网络，它可以在现有神经网络的基础上直接预测细化的姿态。通过使用新颖的数据增强方案进行训练，我们的方法在四个大型姿势估计基准测试中得到了系统的改进。