Joint forecasting of human trajectory and pose dynamics is a fundamental
building block of various applications ranging from robotics and autonomous
driving to surveillance systems. Predicting body dynamics requires capturing
subtle information embedded in the humans' interactions with each other and
with the objects present in the scene. In this paper, we propose a novel
TRajectory and POse Dynamics (nicknamed TRiPOD) method based on graph
attentional networks to model the human-human and human-object interactions
both in the input space and the output space (decoded future output). The model
is supplemented by a message passing interface over the graphs to fuse these
different levels of interactions efficiently. Furthermore, to incorporate a
real-world challenge, we propound to learn an indicator representing whether an
estimated body joint is visible/invisible at each frame, e.g. due to occlusion
or being outside the sensor field of view. Finally, we introduce a new
benchmark for this joint task based on two challenging datasets (PoseTrack and
3DPW) and propose evaluation metrics to measure the effectiveness of
predictions in the global space, even when there are invisible cases of joints.
Our evaluation shows that TRiPOD outperforms all prior work and
state-of-the-art specifically designed for each of the trajectory and pose
forecasting tasks.

本文提出了一种新的基于图注意力网络的轨迹和姿态动力学建模方法，通过消息传递界面融合了输入输出空间中人机和人物交互的不同层次，最终在两个具有挑战性的数据集上评估其性能，证明其优于先前的工作和针对轨迹和姿态预测任务的现有技术水平。

TRiPOD：野外人体行为轨迹和姿态动态预测

TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild

Cooperation among the traffic signals enables vehicles to move through
intersections more quickly. Conventional transportation approaches implement
cooperation by pre-calculating the offsets between two intersections. Such
pre-calculated offsets are not suitable for dynamic traffic environments. To
enable cooperation of traffic signals, in this paper, we propose a model,
CoLight, which uses graph attentional networks to facilitate communication.
Specifically, for a target intersection in a network, CoLight can not only
incorporate the temporal and spatial influences of neighboring intersections to
the target intersection, but also build up index-free modeling of neighboring
intersections. To the best of our knowledge, we are the first to use graph
attentional networks in the setting of reinforcement learning for traffic
signal control and to conduct experiments on the large-scale road network with
hundreds of traffic signals. In experiments, we demonstrate that by learning
the communication, the proposed model can achieve superior performance against
the state-of-the-art methods.

本研究探讨了如何提高道路交通信号灯的效率，提出了一种名为 CoLight 的新模型，使用图形注意网络来帮助交通信号之间的通信，实现了更好的交通信号控制表现，并展示了在大规模道路网络中实现的效果。